text:
\section{Introduction}

\input{figures/Fig1-teaser}

3D object detection is an enabling capability of autonomous driving in unconstrained real-world scenes \cite{wang2022detr3d,wang2021fcos3d}.
It aims to predict the location, dimension and orientation of individual objects of interest in a 3D world.
Despite favourable cost advantages,multi-camera based 3D object detection~\cite{wang2021fcos3d,wang2022probabilistic,wang2022detr3d,zhou2019objects} remains particularly challenging.
To obtain 3D representation,
dense depth estimation is often leveraged \cite{philion2020lift},
which however is not only expensive in computation
but error prone.
To bypass depth inference, more recent methods \cite{wang2022detr3d,li2022bevformer} exploit
query-based 2D detection~\cite{carion2020end}
to learn a set of sparse and virtual embedding for multi-camera 3D object detection, yet incapable of effectively modeling the geometry structure among objects.
Typically, the canonical Cartesian coordinate system with {\em perpendicular} axis is adopted in
either 2D \cite{zhou2019objects,wang2021fcos3d} or 3D \cite{wang2022detr3d,li2022bevformer} space.
This is largely restricted by convolution based models used.
In contrast, the physical world perceived under each camera {\em in the ego car's perspective} is in shape of wedge intrinsic to the camera imaging geometry with radical
{\em non-perpendicular} axis (Figure \ref{fig:polarcardi}).
Bearing this imaging property in mind, we conjugate that the Polar coordinate system should be more appropriate and natural than the often adopted Cartesian counterpart
for 3D object detection.
Indeed, the Polar coordinate has been exploited in a few LiDAR-based 3D perception methods \cite{zhang2020polarnet,bewley2020range,rapoport2021s,zhu2021cylindrical}.
However, they are limited algorithmically due to the adoption of convolutional networks restricted to rectangular grid structure and local receptive fields.

Motivated by the aforementioned insights, in this work a novel {\bf\em Polar Transformer} (PolarFormer) model for multi-camera 3D object detection in a Polar coordinate system is introduced (Figure~\ref{fig:learning_targets}).
Specifically, we first learn the representation of polar rays corresponding to image regions in a sequence-to-sequence cross-attention formulation.
Then we rasterize a Polar BEV representation consisting of a set of Polar rays evenly distributed around 360 degrees.
To deal with irregular Polar grids as suffered by conventional LiDAR based solutions \cite{zhang2020polarnet,bewley2020range,rapoport2021s,zhu2021cylindrical},
we propose a cross-attention based decoder head design without
restriction to the shape of input structure.
For tackling the challenge of unconstrained object scale variation along Polar's distance dimension, we resort to a multi-scale Polar BEV representation learning strategy.

The {\bf contributions} of this work are summarized as follows:
\textbf{(I)}
We propose a new {\em Polar Transformer} (PolarFormer) model for multi-camera 3D object detection in the Polar coordinate system.
\textbf{(II)}
This is achieved based on two Polar-tailored designs:
A cross-attention based decoder design
for dealing with the irregular Polar girds,
and a multi-scale Polar representation learning strategy
for handling the unconstrained object scale variations over Polar's distance dimension.
\textbf{(III)}
Extensive experiments on the nuScenes dataset show that our PolarFormer establishes new state of the art for camera-based 3D object detection (Figure \ref{fig:teaser}), whilst yielding top performance on the BEV semantic segmentation (supplementary material).
\section{Related work}

\paragraph{Monocular/multi-camera 3D object detection}
Image-based 3D object detection aims to estimate the object location, dimension and orientation in the 3D space alongside its category given only image input.
To solve this ill-posed problem,
\cite{zhou2019objects,wang2021fcos3d} naively build their detection pipelines by augmenting 2D detectors~\cite{zhou2019objects,tian2019fcos} in a data driven fashion.
PGD~\cite{wang2022probabilistic} further captures the uncertainty and models the relationship of different objects by utilizing geometric prior.
Contrast to the above image-only methods, depth-based methods~\cite{xu2018multi,ding2020learning,wang2019pseudo,you2019pseudo,ma2019accurate,reading2021categorical} use depth cues as 3D information to mitigate the naturally ill-posed problem.
Recently, multi-camera-based 3D object detection emerges. DETR3D~\cite{wang2022detr3d} considers detecting objects across all cameras collectively.
It learns a set of sparse and virtual query embedding, without explicitly building the geometry structure among objects/queries.
~\cite{li2022bevformer} considers detecting objects in BEV,
performing end-to-end object detection via object queries.
Note that multi-camera setting uses the same amount of training data as the monocular pipelines.
Both multi-camera and monocular paradigms share the same evaluation metrics.
\paragraph{Bird's-eye-view (BEV) representation}
Recently there is a surge of interest in transforming the monocular or multi-view images from ego car cameras into the bird's-eye-view coordinate~\cite{roddick2018orthographic,philion2020lift,li2021hdmapnet,roddick2020predicting,reading2021categorical,saha2021translating} followed by specific optimization tasks (\textit{e.g.},~3D object detection, semantic segmentation).
A natural solution~\cite{philion2020lift,hu2021fiery} is to
learn the BEV representation by leveraging the pixel-level dense depth estimation. This however is error-prone due to lacking ground-truth supervision.
Another line of research aims to bypass the depth prediction and directly leverage a Transformer~\cite{chitta2021neat,can2021structured,saha2021translating} or a FC layer~\cite{li2021hdmapnet,roddick2020predicting,yang2021projecting} to learn the transformation from camera inputs to the BEV coordinate.
A similar attempt as ours is conducted in \cite{saha2021translating} but limited in a couple of aspects:
{\bf (i)}
It is restricted to monocular input for a straightforward 2D segmentation task while we consider multiple cameras collectively for more challenging 3D object detection;
{\bf (ii)}
We uniquely provide a solid multi-scale Polar BEV transformation to tackle the unconstrained object scale variations and followed by a jointly optimized cross-attention based Polar BEV decoder.
\paragraph{3D object detection in Polar coordinate}
3D object detection in the Polar or Polar-like coordinate system
has been attempted in LiDAR-based perception methods.
For example, CyliNet~\cite{zhu2021cylindrical} introduces range-based guidance for extracting Polar-consistent features.
In particular, it adapts a Cartesian heatmap to a Polar version for object classification, whilst learning relative heading angles and velocities. However, CyliNet still lags clearly behind the Cartesian counterpart.
Recently, PolarSteam~\cite{chen2021polarstream} designs a learnable sampling module for relieving object distortion in Polar coordinate and uses range-stratified convolution and normalization for flexibly extracting the features over different ranges.
Limited by the convolution based network, it remains inferior
despite of these special designs.
In contrast to all these works, we resort to the cross-attention mechanism, tackling the challenges of object scale variance and appearance distortion in the Polar coordinate principally.
\section{Method}
In 3D object detection task, we are given a set of $N$ monocular views $\{\mathbf{I_n}, \mathbf{\Pi_n}, \mathbf{E_n}\}_{n=1}^N$ consisting of input images $\mathbf{I_n} \in \mathbb{R}^{H\times W\times 3}$, camera intrinsics $\mathbf{\Pi_n} \in \mathbb{R}^{3\times3}$ and camera extrinsics $\mathbf{E_n} \in \mathbb{R}^{4\times4}$.
The objective of our {\em Polar Transformer} (PolarFormer) is to learn an effective
Polar BEV  representation
from multiple camera views for facilitating the prediction of object locations, dimensions, orientations and velocities in the Polar coordinate system.
PolarFormer consists of the following components.
A {\em cross-plane encoder} first
produces a multi-scale feature representation of
each input image, characterized by a cross-plane attention mechanism in which Polar queries attend to input images to generate 3D features in BEV.
A {\em Polar alignment module} then aggregates Polar rays from multiple camera views to generate a structured Polar map. Further, a {\em Polar BEV encoder} enhances the Polar features with multi-scale feature interaction. Finally, a {\em Polar detection head} decodes the Polar map and predicts the objects in the Polar coordinate system.
For tackling the unconstrained object scale variation with multi-granularity of details,
we consider a multi-scale Polar BEV  representation structure.
As shown in Figure \ref{fig:multi-scale}, image features with different scales have unique cross-plane encoders
and interact with each other in a shared Polar BEV encoder.
Multi-scale Polar BEV maps are then queried by Polar BEV decoder head.
An overall architecture of PolarFormer is depicted in Figure \ref{fig:pipeline}.
\input{figures/Fig2-pipeline}
\subsection{Cross-plane encoder}
The goal of cross-plane encoder is to associate an image with Polar rays. According to the geometric model of camera, for any camera coordinate $ (x^{(C)}, y^{(C)}, z^{(C)})\in \mathbb{R}^3$, the transformation to image coordinate $ (x^{(I)}, y^{(I)})\in \mathbb{R}^2$could be described as:
\begin{equation}\label{eq:cam2img}
    s\begin{bmatrix}
    x^{(I)}\\
    y^{(I)}\\
    1
    \end{bmatrix} =
    \begin{bmatrix}
    f_x & 0 & u_0 \\
    0 & f_y & v_0 \\
    0 & 0 & 1
    \end{bmatrix}
    \begin{bmatrix}
    x^{(C)} \\
    y^{(C)} \\
    z^{(C)}
    \end{bmatrix},
\end{equation}
where $f_x$, $f_y$, $u_0$ and $v_0$ are camera intrinsic parameters in $\mathbf{\Pi}$, $x^{(C)}$, $y^{(C)}$ and $z^{(C)}$ are the horizontal, vertical, depth coordinate respectively. $s$ is the scale factor. For any Polar BEV  coordinate $(\rho^{(P)}, \phi^{(P)})$, we have:
\begin{equation}\label{eq:amizuth}
    \phi^{(P)} = \arctan \frac{x^{(C)}}{z^{(C)}} = \arctan \frac{x^{(I)}-u_0}{f_x},
\end{equation}
\begin{equation}\label{eq:radius}
\begin{split}
    \rho^{(P)} = \sqrt{(x^{(C)})^2 + (z^{(C)})^2}
         =z^{(C)}\sqrt{(\frac{x^{(I)}-u_0}{f_x})^2+1}.
\end{split}
\end{equation}
Eq. \eqref{eq:amizuth} suggests that the azimuth $\phi^{(P)}$ is irrelevant to the vertical value of image coordinate. It is hence natural to build a one-to-one relationship between Polar rays and image columns \cite{saha2021translating}. However, we need object depth $z^{(C)}$ to compute radius $\rho^{(P)}$, the estimation of which is ill-posed. Instead of explicit depth estimation, we leverage
the attention mechanism~\cite{vaswani2017attention} to model the relationship between pixels along the image column and positions along the Polar ray.

Let $\mathbf{f}_{n,u,w} \in \mathbb{R}^{H_u\times C}$ represent the image column from $n$th camera, $u$th scale and $w$th column, and
$\mathbf{\dot{p}}_{n,u,w} \in \mathbb{R}^{R_u \times C}$ denote the corresponding {\em Polar ray} query we introduce, where $H$ and $R$ are the image feature map's height and Polar map's range. We formulate cross-plane attention as:
\begin{equation}
  \begin{split}
  \mathbf{p}_{n,u,w} = \mathrm{MultiHead}&(\mathbf{\dot{p}}_{n,u,w},\mathbf{f}_{n,u,w},\mathbf{f}_{n,u,w}) = \mathrm{Concat}(\mathrm{head_1}, \dots, \mathrm{head_h})\mathbf{W}^O_u, \\
    & \mathrm{where\ head_i} = \mathrm{Attention}(\mathbf{\dot{p}}_{n,u,w}\mathbf{W}_{i,u}^Q, \mathbf{f}_{n,u,w}\mathbf{W}_{i,u}^K, \mathbf{f}_{n,u,w}\mathbf{W}_{i,u}^V),
  \end{split}
\end{equation}
where $\mathbf{W}_{i,u}^Q \in \mathbb{R}^{d_{model} \times d_q}$, $\mathbf{W}_{i,u}^K \in \mathbb{R}^{d_{model} \times d_k}$, $\mathbf{W}_{i,u}^V \in \mathbb{R}^{d_{model} \times d_v}$, $\mathbf{W}^O_u \in \mathbb{R}^{hd_{model} \times d_k}$
are the projection parameters, $d_q=d_k=d_v=d_{model}/h$, $d$ is the feature dimension and $h$ is the number of heads.

Stacking the Polar ray features $\mathbf{p}_{n,u,w} \in \mathbb{R}^{R_u \times C}$ along azimuth axis, we obtain the Polar feature map (\textit{i.e.}, {\em Polar BEV  representation}) $\mathbf{P}_{n,u}$ for $n$th camera and $u$th scale as:
\begin{equation}
    \mathbf{P}_{n,u} = \mathrm{Stack}([\mathbf{p}_{n,u,1}, \dots,\mathbf{p}_{n,u,W_u}], \mathrm{dim}=1) \in \mathbb{R}^{R_u \times W_u \times C },
\end{equation}
where $W_u$ denotes the azimuth dimension.
This sequence-to-sequence cross-attention-based encoder can encode geometric imaging prior and implicitly learn a proxy for depth efficiently. Next, we show how to integrate independent Polar rays from multiple cameras into a coherent and structured Polar BEV map.

\subsection{Polar alignment across multiple cameras}
Our Polar alignment module transforms Polar rays from different camera coordinates to a shared world coordinate. Taking multi-view Polar feature maps $\{\mathbf{P}_{n,u}\}_{n=1}^N$ and camera matrix $\{\mathbf{\Pi}_n,\mathbf{E}_n\}_{n=1}^N$as inputs, it produces a coherent Polar BEV map $\mathbf{G}_u \in \mathbb{R}^{\mathcal{R}_u\times\mathcal{N}_u\times \mathcal{C}}$, covering all camera views, where $\mathcal{R}_u$, $\mathcal{N}_u$ and $\mathcal{C}$ are the dimensions of radius, azimuth and feature.
Concretely, it first generates a set of 3D points in the cylindrical coordinate uniformly, denoted by $\mathcal{G}^{(P)}=\{(\rho^{(P)}_{i},\phi^{(P)}_{j},z^{(P)}_{k})|i=1,\dots,\mathcal{R}_u;j=1,\dots,\mathcal{N}_u; k=1,\dots,\mathcal{Z}_u\}$, where $\mathcal{Z}_u$ is the number of points along $z$ axis. Since cylindrical coordinate and Polar coordinate share radius and azimuth axis, their superscripts are both denoted with $P$. The points are then projected to the image plane of $n$th camera to retrieve the index of Polar ray, estimated by:
\begin{equation}\label{eq:alignment_proj}
    \begin{bmatrix}
    sx^{(I)}_{i,j,k,n} \\
    sy^{(I)}_{i,j,k,n}\\
    s \\
    1
    \end{bmatrix} =
    \begin{bmatrix}
    \mathbf{\Pi}_n & 0 \\
    0 & 1
    \end{bmatrix}
    \mathbf{E}_n
    \begin{bmatrix}
    \rho^{(P)}_i\sin{\phi^{(P)}_j} \\
    \rho^{(P)}_i\cos{\phi^{(P)}_j} \\
    z^{(P)}_k \\
    1
    \end{bmatrix},
\end{equation}
where $s$ is the scale factor. Coherent Polar BEV map of $u$th scale can be then generated by:
\begin{equation}
\label{eq:corherent_sampling}
\begin{split}
    \mathbf{G}_u(\rho^{(P)}_i,\phi^{(P)}_j)
    &=\frac{1}{\sum_{n=1}^N\sum_{k=1}^\mathcal{Z} \lambda_n(\rho^{(P)}_i,\phi^{(P)}_j,z^{(P)}_k)} \\
    & \cdot \sum_{n=1}^N\sum_{k=1}^\mathcal{Z}\lambda_n(\rho^{(P)}_i,\phi^{(P)}_j,z^{(P)}_k){\mathcal{B}(\mathbf{P}_{n,u},(\overline{x}^{(I)}_{i,j,k,n}, \overline{r}_{i,j,n}))},
\end{split}
\end{equation}
where $\lambda_n(\rho^{(P)}_i,\phi^{(P)}_j,z^{(P)}_k)$ is binary weighted factor indicating visibility in $n$th camera, $\mathcal{B}(\mathbf{P}, (x,y))$ denotes the bilinear sampling $\mathbf{P}$ at location $(x,y)$, $\overline{x}^{(I)}_{i,j,k,n}$ and  $\overline{r}_{i,j,n}$ denote the normalized Polar ray index and radius index. Note the radius $r$ is the distance between the point and the camera origin in BEV. Our Polar alignment module incorporates the features at different heights by generating the points along $z$ axis.
As validated in Table \ref{table:coordinate}, learning Polar representation is superior over Cartesian coordinate due to
minimal information loss and higher consistency with raw visual data.

\input{figures/Fig-multi-scale_coord}
\subsection{Polar BEV encoder at multiple scales}
We leverage multi-scale feature maps for handling object scale variance in the Polar coordinate.
To that end,
the Polar BEV encoder performs information exchange among neighbouring pixels and across multi-scale feature maps.
Formally, let $\{\mathbf{G}_u\}_{u=1}^U$
be the input multi-scale Polar feature maps and $\hat{x}_q \in [0, 1]^2$ be the normalized coordinates of the reference points for each qeury element $q$, we introduce a multi-scale deformable attention module \cite{zhu2020deformable} as:
\begin{equation}\label{eq:ms_deform_attn}
 \mathrm{MSDeformAttn}(\mathbf{z}_q,x_q, \{\mathbf{G}_u\}_{u=1}^U) =
 \sum_{m=1}^M \mathbf{W}_m [\sum_{u=1}^{U}\sum_{k=1}^K A_{muqk}\mathbf{W'}_m\mathbf{G}_u(\zeta_u(\hat{x}_q)+\Delta x_{muqk})],
\end{equation}
where $m$ and $k$ are the index of the attention head and the sampling point. $\mathbf{z}_q$ is the query feature. $\Delta x_{muqk}$ and $A_{muqk}$ denote the sampling offset and the attention weight of the $k$th sampling point in $u$th feature level and $m$th attention head. The attention weight $A_{muqk}$ is normalized by $\sum_{u=1}^U\sum_{k=1}^K A_{muqk} = 1$. Function $\zeta_u$ generates the sampling offsets and rescales the normalized coordinate $\hat{x}_q$ to the $u$th feature scale. $\mathbf{W}_m$ and $\mathbf{W}'_m$ are learnable parameters.
Serving as query, each pixel in the multi-scale feature maps exploits the information from both neighbouring pixels and pixels across scales.
This enables learning richer semantics across all feature scales.

\subsection{Polar BEV decoder at multiple scales}
The Polar BEV decoder decodes the above multi-scale Polar features to make predictions in the Polar coordinate.
We construct the Polar BEV decoder
with deformable attention \cite{zhu2020deformable}.
Specifically, we query $q$ in Eq. \eqref{eq:ms_deform_attn} as learnable parameters.

Unlike 2D reference points in the encoder,
here the reference points are in 3D cylindrical coordinate, equal to Polar coordinate when projected to BEV.
The classification branch in each decoder layer outputs the confidence score vector $\mathbf{c}\in \mathbb{R}^{\mathcal{O}}$, where $\mathcal{O}$ is the number of categories. The key learning targets of regression branch are in polar coordinate instead of Cartesian coordiante, as illustrated in Figure \ref{fig:learning_targets}.
For simplicity, superscript $^{(P)}$ is omitted.
Reference points $(\rho,\phi, z)$ are iteratively refined in the decoder.
With reference points, the regression branch regresses the offsets $d_\rho$, $d_\phi$ and $d_z$.
The learning targets for orientation $\theta$ and velocity $v$ are relative to azimuths of objects and separated to orthogonal
components $\theta_{\phi}$, $\theta_{\rho}$, $v_{\phi}$ and $v_{\rho}$, defined by:
\begin{equation}
        \bar{\theta}_{ori} = \theta_{ori} - \phi,\quad \
        \theta_{\phi} = \sin{\bar{\theta}_{ori}},\quad \
        \theta_{\rho} = \cos{\bar{\theta}_{ori}},
\end{equation}
and
\begin{equation}
        \bar{\theta}_{v} = \theta_{v} - \phi, \quad \
        v_{\phi} = v_{abs}\sin{\bar{\theta}_{v}}, \quad \
        v_{\rho} = v_{abs}\cos{\bar{\theta}_{v}}.
\end{equation}
Here, $\theta_{ori}$ is the yaw angle of the bounding box. $v_{abs}$ and $\theta_v$ are the absolute value and angle of velocity. We regress the object size $l$, $w$ and $h$ as $\log l$, $\log w$ and $\log h$.
We adopt Focal loss \cite{lin2017focal} and L1 loss for classification and regression respectively.

\section{Experiments}
\label{sec:exp}

{\bf Dataset}
We evaluate the PolarFormer on the nuScenes dataset~\cite{nuscenes2019}.
It provides images with a resolution of $1600 \times 900$ from 6 surrounding cameras (Figure~\ref{fig:teaser}).
The total of 1000 scenes, where each sequence is roughly 20 seconds long and annotated every 0.5 second, is split officially into \texttt{train/val/test} set with 700/150/150 scenes.
It comes with informative annotations of the map.
We choose five classes (drivable area, pedestrian crossing, walkway, carpark, and divider) for map semantic segmentation.

{\bf Metrics }
For 3D object detection, we follow the default evaluation protocol~\cite{nuscenes2019}.
There are 7 official metrics:
mean Average Precision (\texttt{mAP}),
Average Translation Error (\texttt{ATE}),
Average Scale Error (\texttt{ASE}),
Average Orientation Error (\texttt{AOE}),
Average Velocity Error (\texttt{AVE}),
Average Attribute Error (\texttt{AAE}), and
NuScenes Detection Score (\texttt{NDS}).
Particularly, \texttt{NDS} is the composite of the other indicators for comprehensive judging.

{\bf Implementation details}
We implement our approach based on the codebase \texttt{mmdetection3d}~\cite{mmdet3d2020}.
Following DETR3D~\cite{wang2022detr3d} and FCOS3D~\cite{wang2021fcos3d}, a ResNet-101~\cite{he2016deep}, with 3rd and 4th stages equipped with deformable convolutions, is adopted as the backbone architecture.
The number of cross-plane encoder layer is set to 3 for each feature scale.
The resolution of radius and azimuth for our multi-scale Polar BEV maps are (64,~256), (32,~128), (16,~64) respectively.
We use 6 Polar BEV encoder and 6 decoder layers.
Following DETR3D~\cite{wang2022detr3d}, our backbone is initialized from a checkpoint of FCOS3D~\cite{wang2021fcos3d} trained on nuScenes 3D detection task, while the rest is initialized randomly.
We use the above setting for \texttt{prototype} verification.
To fully leverage the sequence data, we further conduct temporal fusion between the current frame and one history sweep in the BEV space.
Following BEVDet4D~\cite{huang2022bevdet4d}, we simply concatenate two temporally adjacent multi-scale Polar BEV maps along the feature dimension and feed it to the Polar BEV encoder.
We randomly sample a history sweep from [3T; 27T] during training,
and sample the frame at 15T for inference.
T ($ \approx 0.083s$) refers to the time interval between two sweep frames.
We term our temporal version as \texttt{PolarFormer-T}.

{\bf Training }
Following DETR3D~\cite{wang2022detr3d} we train our models for 24 epochs with the AdamW optimizer and cosine annealing learning rate scheduler on 8 NVIDIA V100 GPUs.
The initial learning rate is $2 \times 10^{-4}$, and the weight decay is set to $0.075$.
Total batch size is set to 48 across six cameras.
Synchronized batch normalization is adopted.
All experiments use the original input resolution.
Note our image variant uses the same amount of training data as the monocular pipelines~\cite{wang2021fcos3d} and the multi-camera counterparts~\cite{wang2022detr3d,li2022bevformer}.
Multi-camera and monocular paradigms share the same evaluation metrics.

{\bf Inference }
We evaluate our model on both nuScenes validation set and test server.
We do not adopt model-agnostic tricks such as model ensemble and test time augmentation.

\input{tables/nusc_test}

\subsection{Comparison with the state of the art}

We compare our method with the state of the art on both \texttt{val} and \texttt{test} sets of nuScenes.
In addition to the
{\bf (i)}
\texttt{prototype} setting mentioned in implementation details,
we also evaluate our model in the
{\bf (ii)}
\texttt{improved} setting, with VoVNet (V2-99)~\cite{lee2019energy} as backbone architecture with a pretrained checkpoint from DD3D~\cite{park2021dd3d} (fine-tuned on extra DDAD15M~\cite{packnet} dataset) to boost performance.

Table~\ref{table:state-of-the-art} compares the results on nuScenes \texttt{test} set.
We observe that our \texttt{PolarFormer} achieves the best performance under both the
{\bf (i)}
\texttt{prototype} and
{\bf (ii)}
\texttt{improved} setting
{
in terms of \texttt{mAP} and \texttt{NDS} metrics.
}
With temporal information \texttt{PolarFormer-T} can further boot performance substantially.

Table~\ref{table:nusc_val} shows that our method achieves leading performance on the \texttt{val} set for both \texttt{mAP} and \texttt{NDS}.
Under the \texttt{improved} setting, PolarFormer shines on all metrics except \texttt{mASE} and \texttt{mAAE}.
Again, \texttt{PolarFormer-T} beats the alternative BEVFormer by a clear margin,
indicating the superiority of learning representation in the Polar coordinate.
More qualitative results are shown in supplementary materials.

\input{tables/nusc_val}

\input{tables/ablations}
\subsection{Ablation studies}

We conduct a series of ablation studies on nuScenes \texttt{val} set to
validate the design of PolarFormer.
Each proposed component and important hyperparameters are examined thoroughly.

{\bf Polar \textit{v.s.} Cartesian }
Table~\ref{table:coordinate} ablates the coordinate system.
We make several observations:
\textbf{(I)}
Learning the representation and making the prediction both on Cartesian, Centerpoint~\cite{yin2021center} gives a strong baseline with 0.378 mAP and 0.454 NDS;
After applying circle NMS, Centerpoint* can further improve;
\textbf{(II)}
Our PolarFormer-CC (with Cartesian feature and prediction) outperforms the Centerpoint equipped with specially designed CBGS head~\cite{yin2021center};
\textbf{(III)}
When Polar BEV map is used to feed into a Cartesian decoder head, \textit{on-par} performance is achieved with the post-processing counterpart;
\textbf{(IV)}
PolarFormer, our full model that predicts all 10 categories with one head and without any post-processing procedure, exceeds highly optimized Centerpoint by 1.1\% in mAP and 0.2\% in NDS.
This suggests the significance of \textit{Polar}
in both representation learning and exploitation (\textit{i.e.}, decoding).

{\bf Visualizations }
With the quantitative evaluation in Figure~\ref{perfgap}, \textbf{(I)}
it is evident that our model in Polar coordinate yields better results than Cartesian consistently.
Specifically, Polar outperforms Cartesian by mAP 3.1\% and NDS 0.3\% in nearby area, mAP 1.3\% and NDS 0.7\% in medium area.
\textbf{(II)}
As shown in Figure~\ref{cartesian_det} and Figure~\ref{polar_det}, compared to the Polar map, Cartesian usually downsamples the nearby area (red) with information loss, while upsamples the distant area (green) without actual information added.
This would explain the inferiority of Cartesian.
\textbf{(III)}
Figure~\ref{fig:query_attn} shows the attention map of the decoder query in multi-scale Polar BEV features.
For better viewing, we resize the multi-scale features into the same resolution.
The bottom/top corresponds to the largest/smallest scale features.
The \textit{y} axis represents the radius of the Polar map.
It is shown that larger objects represented in the small map (top) are close to the ego car (small radius) whilst small objects in the large map (bottom) distribute through the distant area.
This is highly consistent with the geometry structure of raw images
(Figure~\ref{fig:teaser}), which has shown to be a more effective coordinate
for 3D object detection as above.

{\bf Architecture }
\textbf{(I)}
We first evaluate three designs of positional embedding (PE):
2D learnable PE, fixed Sine PE, and 3D PE (generated based on a set of 3D points for each Polar ray position).
\textbf{(II)}
As our cross-plane encoder transforms different levels of feature from FPN into Polar rays independently, we can fuse the multi-level features into a single BEV or naturally shape multiple BEVs with different \textit{or} same resolutions;
Table~\ref{table:multi-scale} clearly shows that a model with multi-scale Polar BEVs outperforms the single-scale counterpart under either coordinate.
In contrast, little performance gain is achieved from multi-scale features in Cartesian.
This suggests that object scale variation is a {\em unique} challenge with Polar, but absent with Cartesian.
Our design consideration is thus verified.
\textbf{(III)}
Table~\ref{table:ablation_model_arch} shows the results of our multi-scale variant with different numbers of Polar BEV encoder and decoder layers;
We also study the effect of sharing the resolution by the multi-level features;
Both contribute to the efficiency of our model;
\textbf{(IV)}
We study the resolution of polar map by adjusting the \textit{azimuth} $\bm{\mathcal{N}_1}$ and \textit{radius} $\bm{\mathcal{R}_1}$  (the number of Polar query in the cross-plane encoder);
Table \ref{table:resolution} shows that the \textit{angle} with 256 and \textit{radius} with 64 gives the best performance.

{\bf The coordinate choice for object prediction and loss optimization }
Until now, we have shown that making the predictions in the Polar coordinate is crucial and superior over in the Cartesian counterpart.
We conjugate that
this is because with object locations projected under the Polar coordinate, a better distribution of reference points can be learned due to taking a more consistent perspective \textit{w.r.t}{} the multi-camera image observation.
Interestingly, we note a discrepancy in the coordinate choice
between {\em object location} prediction and loss optimization.
In particular, we find that when optimizing the object localization loss in the Polar coordinate, the loss converges very slowly and a significant performance degradation is also observed (Table \ref{tab:coord_target_loss}).
A plausible obstacle is the numerical discontinuity between 0 and $2\pi$ in the azimuth, which however is continuous in a physical world.

\input{figures/Fig-exp-CP}
\input{figures/Fig-model_arch-attention}
\input{tables/coords_target_loss_time_window}

\subsection{BEV segmentation}
\input{tables/nus_seg_val}

For BEV semantic segmentation, we use intersection-over-union (\texttt{IoU}) to assess the performance.
Following LSS~\cite{philion2020lift}, we create a binary image prediction for each element based on a specific threshold to evaluate with the ground truth images.

We use the same backbone as~\cite{philion2020lift}, \textit{i.e.}, EfficientNet-B0~\cite{tan2019efficientnet} pre-trained on ImageNet to align with OFT~\cite{roddick2018orthographic} and LSS~\cite{philion2020lift}.
We use the output of the Polar BEV encoder to predict the segmentation mask in Polar coordinate.
We use the initial learning rate of $3 \times 10^{-4}$ and weight decay of $0.075$.
We train the segmentation model for 24 epochs with the AdamW optimizer and the cosine annealing learning rate scheduler.
The total batch size is set to 48 across six cameras.
Different to LSS~\cite{philion2020lift} and BEVFormer~\cite{li2022bevformer}, we do not evaluate the two categories of \texttt{Car} and \texttt{Vehicle} as they are already predicted by the detector.
Instead, we focus on the segment categories of \texttt{Crossing}, \texttt{Walkway} and \texttt{Carpark} (see Figure~\ref{fig:supp_vis_3}).

Table~\ref{tab:nus-seg_val} summarizes the BEV semantic segmentation results on the nuScenes \texttt{val} set.
It is evident that our method achieves the best performance among all the competitors on all the classes.
In particular, this is achieved even by using a more efficient and lightweight backbone EfficientNet-B0~\cite{tan2019efficientnet}
than ResNet-101 used by BEVFormer~\cite{li2022bevformer}.
Additionally, we equip the model with both detection and segmentation heads for multi-task joint training.
In this setting,
we use ResNet-101 as the backbone for more capacity.
As shown in the last two rows of Table~\ref{tab:nus-seg_val}, our PolarFormer again outperforms ResNet-101 based BEVFormer-S in this multi-task learning setting, further indicating the superiority of conducting BEV representation based perception tasks in the Polar coordinate.
Figure~\ref{fig:supp_vis_3} shows the results of 3D detection and BEV segmentation.
\input{figures/Fig-exp-vis}
\section{Conclusions}

We have proposed the Polar Transformer (PolarFormer) for 3D object detection in multi-camera 2D images from the ego car's perspective.
With a rasterized BEV Polar representation geometrically aligned to visual observation,
PolarFormer overcomes irregular Polar grids by a cross-attention based decoder.
Further, a multi-scale representation learning strategy is designed
for tackling the intrinsic object scale variation challenge.
Extensive experiments on the nuScenes dataset validate
the superiority of our PolarFormer over previous alternatives on 3D object detection, as well as more generic ability on BEV semantic segmentation task.
\section{Appendix}

\subsection{Visualizations}
We visualize our 3D object detection and BEV semantic segmentation results in Figure~\ref{fig:supp_vis_1} and Figure~\ref{fig:supp_vis_2}.

redpajama_set_name: RedPajamaArXiv


text:
The crew of the enterprise respond to a distress signal and attempt to rescue stranded crew members on the planet Tarkana IV, birthplace of Tasha Yar. When they encounter one of the warring factions, the coalition, it is soon revealed that among them is Tasha's Sister, Ishara. The crew and Data in particular are faced with having to reopen the wound Tasha's death left behind while getting to know this unique and different relative of hers who is trying to aid them in retrieving the captured crew.
Doctor Crusher is faced with a peculiar mystery. Everyone she knows and loves is disappearing. When she realizes the very universe around her is collapsing she's faced with some interesting challenges and we get some exciting surprises. Find out what we think of this episode of Star Trek the Next Generation.
Suddenly human
Captain Picard and crew receive a distress call from a damaged Telarian vessel and respond only to find that one of the crew members is a human who was thought to have died many years ago in an invasion. It turns out to be a very complicated situation for ship and crew and especially for Captain Picard. Find out what we think about this exciting episode of Star Trek the Next Generation.
Young Mr. Potts is in deep trouble because his prank on his brother went terribly wrong. His little brother is in deep trouble after eating a poisonous plant on the holo-deck. Fortunately they are near a starbase that can mend his little brother quickly. Unfortunately Data freaks out and steals the enterprise. Data flies her to a planet that registers but one life form that turns out to be, Dr. Noonien Soong (Data's father.) Turns out Dr. Soong activated a homing beacon that forces his creations to come home - but Data isn't the only Soong Android that comes home to call. Willy Potts has only 24-36 hours and the crew struggle to regain control of the ship... Lore and Data are back together and when it is revealed that Dr. Soong wished to give Data basic human Emotions, Lore deviously plots to take the chip for himself. Lore tricks Dr. Soong and takes Data's emotion chip for himself, somehow making him more evil than he was and when Soong realizes what has happened A struggle ensues, which sadly leads to Dr. Soong's death. (He was already dying) and Lore stealing. The Crew of the Enterprise finally figure out a way to get to the surface but it is too. Fortunately Data's memory is restored and after a final moment with Dr. Soong the crew are back on course to save young Willy Potts. The question remains, can brothers forgive each other.
While the Enterprise is docked at McKinnly Station, the Crew take the time to make necessary repairs after the Borg conflict and for for much needed shore leave on Earth. Captain Picard takes this opportunity to go home to his birth village of Avar France and stay on his family's vineyard, Run and kept in the traditional way, by his older brother, Robert. Meanwhile Worf's parents are taking this opportunity to visit the enterprise to make sure their son is okay after his own trauma, discommodation from his people. As if you can't get any more sentimental, Wesley also receives a message from his long dead father via a recording he made when Wesley was still an infant. Both Picard and Worf have to face their inner truths about what happened to them and we get to discover more about what makes these iconic characters tick.
The Best of Both Worlds Part II
The conclusion to our discussion of The Best of Both Worlds with Part II, and the beginning of Season 4!
Season 3 recap
Some of the Starfleet Boy team discuss Season 3 of Star Trek the Next generation. We'd love to hear what you think of the season as a whole! - Love Starfleet Boy.
The Best of both worlds Part I
The crew of the Enterprise encounter the signature destructive mark of the Borg and suddenly the situation escalates rather quickly as Starfleet forms an armada to stop the oncoming threat. The Borg prove to be a formidable (beyond the wildest dreams of the crew) opponent and they manage to take Captain Picard away from the crew to use him as a weapon against humanity and the Federation.. Find out what we think of the last episode of season 3 of Star Trek the Next Generation.
Transfigurations
Doctor Crusher and the crew are part of a rescue mission in which they are able to save a severely injured Alien and bring him back to health, but his rapid healing suggests there's something else at work beyond the medical attention he's receiving. The Alien is unable to remember who he is so Beverly names him John Doe for the time being. We find out rather quickly that something is happening to John that's bigger than even he is aware and he is the first among his species to evolve. Lots of interesting concepts arise in this exciting episode.
Ménage à Troi
The whole team have a light hearted jaunt to Betazed where a Ferengi delegation's leader (Daimon Tog) shows an obsessive interest in Lwaxana Troi. Couple that with young Wesley preparing for his oral exam which will finally grant him entry to Starfleet Academy. Find out what we think of this rather humorous episode of Star Trek the next Generation.
Ambassador Sarek has been working for much of his career to negotiate with the Lagarens and really only he is able to conduct the negotiations however he is suffering from a rare Vulcan illness that effects the mind and causes Vulcans to loose control of their emotions. Captain Picard and crew must try to convince Sarek (sensitively) that he should postpone the negotiations but he is unwilling to do so. Finally a solution is found in which Picard and Sarek mind meld to afford Sarek the stability he requires. Find out what we think of this epic and monumental episode of Star Trek the Next Generation.
The Most Toys
Data is kidnapped and is driven to one of the most difficult decisions of his life. Find out what we thought of this episode of Star Trek the Next Generation.
Hollow Pursuits
Reginald Barkley feels left out and as a result he retreats to the comfort of the holodeck, until it starts to interfere with his job. Geordi is now tasked with both trying to figure out how to integrate Barkley as a member of the team and solve a problem that's putting the Enterprise at great risk. Will helping Barkley also help save the ship? Find out what we think of this episode of Star Trek the Next Generation.
Tam Elbram is an extraordinary telepath however the tradeoff is extreme loneliness and misunderstanding by non-telepaths (and telepaths alike) until he meets the mysterious Tin Man, a creature that is the last of its kind. The crew of the Enterprise must attempt to make first contact with this remarkable being while thwarting the attempts of the Romulans who see it as a tactical advantage. Find out what we think of this exciting episode of Star Trek TNG.
Captain Picard is stressed out and at the persistent encouragement of his crew decides to take a holiday to Risa. Little does he know that there's a degree of predestination and adventure waiting for him as well as some unexpected romance. Find out what we think of this exciting episode and let us know your thoughts as well!
Captain Picard is kidnapped and replaced by a duplicate that isn't quite right. The crew must struggle with their loyalty and their suspicions and the real captain Picard must figure out how to escape his captivity. Find out what we think of this exciting episode of Star Trek the Next Generation with special guest star Gary from Trekkerprise.
Star Trek IV - The Voyage Home
A somewhat impromptu next step in our discussions and recollections about the Star Trek film series. Here we find Captain Kirk and crew in a lighthearted film dealing with extremely heavy topics and pulling it off beautifully. We come at last to Star Trek IV, the Voyage home. Find out what we think about this wonderful and important film in both Star Trek history and film history alike.
What starts out as a seemingly routine crew exchange program turns into a wild ride through Klingon politics and honor. Worf makes the ultimate sacrifice for his people in this exciting episode. Join us as we discuss, Sins of the Father.
Data creates a child and her journey through life is not easy, find out what we think of this episode of Star Trek the Next Generation.
An strange event causes the crew of the Enterprise D to meet the Crew of the Enterprise C, however their presence in the future has severely altered the way things are supposed to be. Find out what we think of this episode and let us know what you think also!
Riker is accused of Murder and the crew must figure out a way to use the evidence to clear his name. Find out what we think of this episode of Star Trek the Next Generation.
Déjà Q
A people in peril as their moon begins to plummet toward their planet and then Q shows up! Join us as we discuss another amazing Q episode on Starfleet boy, we'd love to hear what you thought of this episode as well!
The High Ground
Doctor Crusher is kidnapped by a group of terrorists on Rutia IV after providing medical aid to people injured in an attack. The crew of the Enterprise fight to save her life and grapple with the internal conflicts of the Rutians.
Roga Danar an engineered soldier who has been exiled by his people to a prison planet escapes while the Enteprise is visiting the leaders of Roga's home planet, Angosia for consideration as a Federation member. At first the crew treat him like a criminal but they quickly discover there's more to him and that the Angosians are not as ready as they think to join the Federation. Find out what we thought of this episode of Star Trek the Next Generation.
The Defector
Shakespearean like drama as a defector is granted safety aboard the Enterprise from the Romulan empire. The defector claims he has vital information about an offensive that is being mounted against the federation but when the crew are suspicious of his facts, he finally reveals himself to be Admiral Jarok and agrees to give any information necessary to convince the crew of the threat. The Enterprise investigates the Admirals claims only to find themselves in an ambush against two Romulan warbirds. Fortunately Captain Picard has a few tricks up his sleeves. Find out what we thought of this exciting episode of Star Trek the Next Generation.

redpajama_set_name: RedPajamaCommonCrawl


Please tell us about your journey in the Comments section below. Share your challenges, morning routines, and plans for starting fresh in 2019.
... this week walking around the Outdoor Retailer Summer Market in Denver, drooling over all the awesome new gear coming to the backpacking world in 2019.
I see the Christmas Craft Shows are starting so if you see me out and about come over to say hello.
With thousands of acres of nature just down the street this is one awesome location!!! Trails for walking, lakes for swimming, what more can you ask for ?
Where To Go in Croatia | Pin Me For Later!
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I'm not sure how many people's bucket list has "stare down a tsunami," on it, but the fellow in this video can go ahead and check off that item. Putting aside common sense, the boat lingers close to an iceberg avalanche, which rapidly displaces enough water to create a localized tsunami. Which is a Japanese term for a terrifyingly huge wave.
We've posted about iceberg avalanches before, but in that video, the people were rightly frightened of the huge wave coming toward them. The man behind the camera seems remarkably calm as the wave looms, right before retreating to the boat's cabin as the crashing water floods the boat.
Avalanches are one of nature's most raw expressions of power. Thousands of tonnes of ice collapsing at once can have unpredictable results and mass destruction.
While the video is definitely breath-taking, the gentleman filming took a massive risk lingering that close to an iceberg avalanche. He was lucky to escape with his life, and a souvenir that reveals the immense power of mother nature.
For more avalanche action, check out this epic mudslide in BC, or a land-based snow avalanche captured on video.


Hi UPPERROOM Parents! Baby Dedication is coming up Sunday, May 19th during the morning service only. Please register your little love to be dedicated: HERE. Registration will be open April 22nd-May 6th. Come join us as we celebrate our growing family!


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Our air conditioning and heating system experts arrive quickly with well-stocked vehicles to ensure that your HVAC issues are resolved fast. We also provide free estimates and set up the appointment at convenient times 7 days a week. Call us for details.


A few months ago, I made a Nutella Cake for my friend Becca's birthday. I also made her Hazelnut Nutella Sandwich Cookies that I decorated the cake with, and that she could give out to friends to celebrate. I posted the recipe for the cake, but never for the cookies! Very bad blogging behavior. I've been getting a few requests for this cookie recipe, and I decided it was about time I responded, just in time for holiday festivity baking. This is a pretty standard shortbread dough, with ground hazelnuts mixed into the batter. You can eat them plain, dip them in dark chocolate, or sandwich them with whatever your favorite spread is – I used a vegan nutella that I had, but any chocolate, vanilla, or even fruit spread would pair well. Enjoy!
*I often put my nuts in the food processor, and grind until they are nearly a flour – but not so much that they turn into paste/butter-like consistency.
Preheat the oven to 350 degrees. Line 2 baking sheets with parchment paper or baking mats.
Combine Earth Balance and sugar in a large bowl. Cream the mixture together until light and fluffy.
Add 2 1/4 cups of the flour and mix until blended. Gradually add the remaining 1/4 cup flour by the tablespoonful as needed to make a smooth dough.
Stir in the hazelnuts until incorporated.
Transfer the dough to a lightly floured surface. Pat it into an oblong shape, then roll it out with a floured rolling pin to a thickness of 1/2 inch.
Using a knife, cut the dough into 2 1/2-in squares, or use whatever cookie cutter shape you want!
Using a lightly floured metal spatula, lift the cookies and slide them off onto the prepared pans, placing them at least 1 inch apart.
Bake on the 2 middle racks of the oven for 15 minutes. Rotate the pans and bake for another 10 minutes, or until the cookies are light golden brown.
Let cool completely on the baking sheets, set on wire racks. Store in an airtight container.
For Becca's birthday I made her something that I knew she would love. I've been planning it for awhile, knowing that her birthday coincided with her first day of classes (she's starting school to become an American Sign Language interpreter!). She wanted me to make her something that she could take with her, to celebrate her birthday with her new classmates. Since she has to commute, she needed something easy to pack and carry, so it was easy to decide to make sandwich cookies. But, you can't have a birthday without a birthday cake, and I made her a cake to match her cookies. I know that Becca loves nuts, especially hazelnuts, so that had to be included in the recipes. The cake I made had three layers of Vanilla Sponge Cake, with a rich Hazelnut Creme in between each layer. Mmm. And then the whole cake was smothered in vegan Nutella Buttercream Frosting.
I sandwiched together the Hazelnut Shortbread Cookies with a dark vegan Nutella cream in the middle. Because she's studying ASL, I made them in the shape of hands! Aren't they cute? I put some of them around the outside of her cake for extra decoration, and to unify the two birthday desserts.
I've had a request for the Nutella Cake recipe, so I'm going to do my best to post it here. Unfortunately, the vegan nutella itself was not homemade, so I can't share a brilliant recipe for that with you. In Holland, they have vegan nutella (not the brand name variety) stocked in all grocery stores. When I went there this summer, I couldn't resist bringing back a few jars with me. I've already nearly run out – I wish I could just go back and get some more but the fact that I just started graduate school means that I just don't have the time (or the money, let's be honest). If you don't have the luck to have been in Holland recently, you can find a recipe for vegan nutella here, it looks delicious!
The cake itself was a vanilla Sponge Cake from Hannah Kaminsky's cookbook My Sweet Vegan.* She uses it as a base for her Green Tea Tiramisu. I just doubled the recipe for cake batter and baked it in three 9-inch round pans. It is a delicious recipe; a dense (but not heavy) golden cake that certainly holds it's own – it's sweet and bright flavor makes it easy to adapt since it pretty much supports any flavor you want it to. I'd say it actually brings out more complex flavors in whatever you combine it with. It would be great with a light fruit sauce or jam spread on it, but since this was going to be a birthday cake, I had to make it rich! I made a Hazelnut Creme to go in between the layers, and then made a more traditional vegan Buttercream that I then added the vegan nutella to. The recipes for the spreads are below. If you can't find hazelnut butter in your grocery store, you can buy regular hazelnuts or filburts and roast them in your oven till they are lightly toasted. Grind them in a food processor until they reach a smooth peanut butter consistency.
Cream Earth Balance with electric beaters in a medium sized bowl. Add the hazelnut butter and combine thoroughly. Slowly add the confectioner's sugar, beating in between additions until fluffy. Add the almond milk very slowly while continuing to beat with electric beaters, until creme has reached a spreadable consistency.
Cream Earth Balance and shortening with electric beaters in a medium sized bowl. Add the nutella and continue beating. Add the confectioner's sugar and flour, and beat until fluffy. Add the almond milk and combine thoroughly. Spread on top of cooled stacked cakes.


text:
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in Reading Is Fundamental
Ten exciting books that are guaranteed to start your year off with a bang
by QWEERIST editor December 19, 2018, 12:57 pm 1.5k Views
We don't know about you, but there's something special about getting all snug with a blanket and a cup of tea as you dive right into a gripping book. There have been some spectacular books released recently with so many of them still for us to discover.
If one of your New Year's Resolutions is to read more, then you've come to the right place. We've rounded up some of our current favourites alongside our most anticipated for 2019 books, ranging from memoirs to poetry to other-worldly fiction. Hopefully, this selection of books will really get your reading off to a kick start. Just a heads up, this post contains affiliate links. For more information, see our disclosure page.
L.C. Rosen – Jack of Hearts (And Other Parts)
Described as being "unapologetically honest, refreshingly bold, and gloriously celebratory" by Simon James Green (author of 'Noah Can't Even'), 'Jack of Hearts' is a confidently queer story that breaks away from the stereotypes to create refreshing and relatable characters.
17-year-old Jack is unapologetically queer. He doesn't care how he comes across, he's sexually active, he wears make-up and he loves spending his money. Thanks to his bold attitude, his schoolfriends convince him to start a dating advice column. As the column starts to rise in popularity, so does attention on Jack. And it's true what they say, not all attention is good attention.
Jack starts to receive anonymous love letters in his locker. What once seemed like a fun joke takes a sinister turn when the notes get more threatening and dark. It's perhaps the only book that blends school life with a detective case and sex advice but we absolutely love it for it. This is the book our teenage selves would have LIVED for.
Buy from Waterstones or Amazon.
Fiona Mozley – Elmet
As a debut novel, Mozley's 'Elmet' has all the qualities of a modern-day classic and shows a writer with a distinctive voice.
Told through the magical feel of an old folktale, 'Elmet' follows a family in a corner of Yorkshire as they come to realise their life isn't what it seems. As children, Cathy and Daniel are expected to roam the woods freely and play, yet are forced by a turn of events to quickly grow up in order to protect themselves.
Shortlisted for the Man Brooker Prize 2018, 'Elmet' won the Polari First Book Prize 2018 and is a sense of something really special. Lyrically beautiful, it's a timeless story of family loyalty and life on the margins.
Melanie Brown – Brutally Honest
If you're one of the thousands that have bought tickets to the hugely-anticipated Spice Girls UK tour, then 'Brutally Honest' by Scary Spice herself is a must-read. The book is a tough read at times, as it chronicles Melanie Brown's history with drugs and abusive relationships, but it's also a necessary one and shows the importance in coming out stronger and wiser from it all.
In a recent interview with Allison Kugel, Brown said of being in an abusive relationship: "When you're in this situation, you think it's only happening to you. It's only when you get out and get to safety that you realize how bad this relationship was, how wrong it was."
This will provide any diehard Spice Girls fan with a look behind the showbiz curtain of pop superstardom that we often never get a glimpse at – warts and all.
Lauren Bravo – What Would the Spice Girls Do?
Speaking of the Spice Girls, this book by Lauren Bravo is a must-have for any fan of the band but also anyone interested in feminism and "girl power".
The Spice Girls showed us how girl power can be about girls working together and the power of friendship. With a little bit of zig-a-zig-ah, 'What Would the Spice Girls Do' is an interesting read on the power the band had on a generation of fans and how that particular brand of feminism continues to be relevant today. If you were around in the 90s, grab yourself this slice of nostalgic power.
Greg Howard – The Whispers
According to Greg Howard himself, this is the most personal story he's ever written.
It's a coming-out story that will resonate with readers as much as it will astound and excite them. Before his mother disappeared, Riley's mother would tell him stories of the Whispers, mysterious creatures that live in the wood with magical powers.
Of course, it was just a fairy tale then… But, could there be something more real to it than Riley could have ever imagined? Described as being timeless yet progressive for its treatment of themes such as religion and sexuality, 'The Whispers' is rightfully creating a lot of buzz ahead of its release in early January.
Andrea Gibson – Lord of the Butterflies
Non-binary poet, activist and artist Andrea Gibson brings together a vivid collection of poetry spanning across emotions and poignant moments in life.
'Lord of the Butterflies' brings together Andrea's unique and delicate vision of gender, romance, loss and family and is a delight to behold. Andrea's poetry is likely to strike to the core with anyone who can find the beauty and power within words. It's an emotional breath of air to anyone needing to know they're not alone in the world.
Kate Davies – In At The Deep End
Three words to describe this book are flirty, filthy, and fun.
Having not had sex for three years, Julia is about to encounter a whole chain of events that will see her 'In at The Deep End'. From being accused of breaking a one-night stand's penis (!!) to wondering whether trimming her pubes makes her a bad feminist, Julia is about to discover that she could have been looking in the completely wrong direction in her quest for love as a sexually-confident lesbian comes into her life.
'In At The Deep End' may be Kate Davies debut novel, but if it's anything like the first reviews are indicating then it could be a confident introduction to promising new talent.
M.K. England – The Disasters
Praises for its representations of Muslim, South Asian, trans, and bisexual characters, 'The Disasters' is a sci-fi delight for YA readers that'll prove that it's more than okay to be regarded as something a little bit different.
Marketed as a mix between 'The Breakfast Club' and 'Guardians of the Galaxy', 'The Disasters' is set in 2194 and follows pilot Nax Hall as he's kicked out of the notorious Ellis Station Academy. Encountering a terrorist attack at the Academy, he steals a shuttle alongside three other Academy washouts. That's, however, the least of the newly-formed gang's worries as they're now being framed for the attack and need to step up and show their worth in order to save the day.
Joe Heap – The Rules of Seeing
Two women meet by chance and are drawn into each other's lives in more ways than one. Nova has been blind since birth and has arrived at a hospital for radical surgery which could give her sight back, it's here where she meets Kate, an architect in a dysfunctional marriage with an abusive husband.
As cliche as it may sound, 'The Rules of Seeing' is a beacon of light and has the power to change perceptions by looking further than you may imagine possible. It's a mesmerising, riveting, and addictive read.
Kate Atkinson – Transcription
'Transcription' has been getting rave reviews since its release in the summer, but if you've not yet read Kate Atkinson's latest then you should really give it a go – it'll be hard to put down.
Focusing on a young woman, Juliet Armstrong, as she works for MI5 during the Second World War, the book is surprisingly light and charismatic. As the tension builds, we start to suspect that not is all as it seems and that there could be a case of identity theft going on.
It's a gripping read that puts identity into question and while it may make you suspect everyone and everything in real life, it'll be worth it for the thrill.
Got any top reads that you think we should know about? Share your suggestions!
Written by QWEERIST editor
YAAS!
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in Life, Reading Is Fundamental
Mel B on sex, drugs, abuse, and healing: 'When you're in an abusive relationship, everyone is isolated from you'
in Entertainment, News
Spice Girls' iconic bus from Spice World movie turned into Airbnb hotel
in News, Reading Is Fundamental
Tegan and Sara announce details on upcoming 'High School' memoir
in Drag Race
Four guest judges for RuPaul's Drag Race UK announced, including Spider-Man star
Melanie C and Sink the Pink join forces for European tour
in History, Reading Is Fundamental
LGBT History Month: Fifteen essential reads on queer history
What does Taiwan's 'no' vote to same-sex marriage really mean for the Taiwanese LGBTQ+ community?
Courtney Act in bid to become Australia's entry for Eurovision Song Contest 2019
Ezra Furman records new original songs for Netflix's 'Sex Education'
© 2018 QWEERIST
Reading Is Fundamental
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The name is Brittany Britalynn. Right now I am looking for friends and if it becomes more than awesome!
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Im so excited to be apart of something powerful. Im everyout going and like trying new thing, am i too young for internet dating i like to joke around and just laugh but know when to be serious! Getting along with everyone is a big deal for me. Looking to meet some cool girls. The information you provide will be used by Match.


text:
Q: Label controls don't show up in panel on inherited form (all other controls do) I realize there have been similar questions asked here before, but after scouring the site I've not found anything that addresses the specific issue I'm experiencing.  I'm using C# in VS 2017.  I have a base form and am trying to create/use an inherited form.  The base form (frmDataEntry) contains several controls (a ListView, a ComboBox, two Labels, four Buttons, and a Panel.  The inheriting form (frmEditRanch) adds six controls to the panel:  three Labels, two Textboxes, and a PictureBox.
All of these controls appear and function as expected, except for the labels inside of the panel.  Labels outside the panel are fine, as are the Textboxes and picturebox inside it; but the Label controls inside the panel do not show up.  I'm stumped.
I've seen in other posts that the child form needs to create the controls in code, so I've tried that as well (creating the labels and adding them to the panel in code), but the result is no different.
Not sure how much code is needed to be helpful, but here is the initialization code that may be of some use.
BASE FORM:
public enum FormState { Idle, Adding, Editing }

public partial class frmDataEntry : Form
{
    protected AgData db = new AgData();
    protected Ranch ranch;
    public const int vScrollBarWidth = 21;

    public frmDataEntry()
    {
        InitializeComponent();
    }

    public frmDataEntry(int RanchID) : this()
    {
        ranch = db.GetRanch(RanchID);
    }

    protected void frmDataEntry_Load(object sender, EventArgs e)
    {
        if (DesignMode) return;
        UpdateRanchNameComboBox();
        cbRanchName.SelectedItem = ranch?.Name;
        ResetDataFields();
        SetFormControls(FormState.Idle);
        CreateListViewHeaders();
        UpdateRecordList();
    }
 ...

CHILD FORM:
public partial class frmEditRanch : frmDataEntry
{
    EditMode mode;

    public frmEditRanch(EditMode editMode, int RanchID = 0) : base(RanchID)
    {
        InitializeComponent();
        mode = editMode;
    }

    private void frmEditRanch_Load(object sender, EventArgs e)
    {
        AddDataControls();
    }

    protected override void AddDataControls()
    {
        Label lblName = new Label();
        lblName.Font = new Font(this.Font.FontFamily, 12, FontStyle.Regular);
        lblName.Text = "Name";
        lblName.TextAlign = ContentAlignment.MiddleLeft;
        lblName.AutoSize = false;
        lblName.Size = new Size(45, 20);
        lblName.Location = new Point(10, 23);
        pnlDataControls.Controls.Add(lblName);

        ...

        pnlDataControls.Refresh();

    }

 ...

redpajama_set_name: RedPajamaStackExchange


text:
Luis Mancheno
Uma Menon
An immigrant from Ecuador, Luis discusses his experiences of persecution and violence for being LGBTQ+, which led him to seek asylum in the United States. He describes his experience being outed to family, the decline of his mental health, and his relationship to faith, as well as the process of seeking an education and asylum.
Narrator: Luis Mancheno
Location: Brooklyn, NY (Virtual)
Content Warning: Descriptions of suicidal thoughts, violence, and homophobia.
Summary: An immigrant from Ecuador, Luis discusses his experiences of persecution and violence for being LGBTQ+, which led him to seek asylum in the United States. He describes his experience being outed to family, the decline of his mental health, and his relationship to faith, as well as the process of seeking an education and asylum.
Topics: Family, Religion, Sexuality, Mental Health, Violence, Discrimination, Politics, Education, Asylum-Seeker, Career, Historical Context
Section 1: 0:00-14:18
Family - Grew up in Quito with parents and sisters; grandmothers helped raise him.
Religion - Parents converted to Evangelicalism although the rest of the family and most of Ecuador was Catholic.
Spent a lot of time during his childhood at church and Bible studies.
Sexuality - Felt different from others; realized that he was attracted to men but felt like he needed to suppress this.
Section 2: 14:18-25:34
Mental Health - Felt trapped by inability to express his identity.
Sexuality - While visiting the US, he was able to meet other gay men through chatrooms.
Met with a gay friend while in Quito, but his aunt had seen him there.
Religion - His aunt forced him to "confess" to being gay, which was seen as a religious sin.
Sexuality - His aunt told his parents, against his will that he was gay.
Religion - Led him to start to think that maybe he was a "sinner" like his family portrayed him to be.
Mental Health - Considered taking his own life
Family, Violence - Physically assaulted by his father after finding out he was gay.
Religion - Asked God to change him so that he was no longer gay.
Religion, Sexuality, Discrimination - Pressured by family to attend conversion therapy.
Sexuality - Family thought he was "cured".
Violence, Sexuality - Someone had tried to murder him by making his car crash, but he survived.
Violence, Discrimination - The perpetrator wrote a homophobic slur on the car; it was a hate crime.
Religion - Asked God to "erase the word gay from [his] life".
Politics - Police did not file a report because they claimed not to have jurisdiction over the gay bar.
Education - Sought a program to study abroad so that he could escape the trauma at home.
Financial Struggles - Had to work a night shift job while studying full-time to be able to sustain himself.
Section 7: (Part 2 of Recording) 0:00-11:45
Sexuality - Excited to see LGBTQ+ culture much more celebrated and normalized in the US.
Asylum-Seeker - Applied for asylum in the US while on his study abroad semester.
Family - While waiting for the asylum application result, his grandfather passed away.
Family - Soon after, his father was diagnosed with Stage Four Cancer; sought permission from the government to visit him.
Family - Took care of his father in his last days; developed a new and strengthened relationship with him.
Sexuality - His father expressed in his last days that he was proud of Luis and accepted his sexuality.
Career - Attended law school and became a lawyer representing immigrants; uses his skills to promote justice and prosperity.
Family - Got married in 2015 with 40 of his family members present.
Historical Context - Interview to be released on January 20, 2021 (Inauguration Day of President Biden and Vice President Harris), a historical moment when the US comes closer to its ideals.


text:
Michelle Rodriguez Age, Net Worth, Sister, Husband, Wiki, Bio.
November 10, 2019 November 10, 2019 - by Admin - Leave a Comment
Mayte Michelle Rodriguez is an American actress and model. Also, known Michelle Rodriguez Age, Net Worth, Salary, Sister, Husband, Boyfriend, Family, Fact, Wiki, Biography.
About Michelle Rodriguez:-
Mayte Michelle Rodriguez is an American actress, voice actress, and model. She was born on July 12, 1978, in San Antonio, Texas, United States. Michelle Rodriguez's current age 41 years as of 2019.
In San Antonio, Texas, United States she was born in an Atheist to believe family in 1978. Also, his Birth Name is Mayte Michelle Rodriguez and his nickname is Mayte, MRod, The Tough Girl, ToughM, Michelle Rodriguez.
Michelle's Nationality is American, Religion is Atheist, Zodiac Sign is Cancer, Sexuality is Bisexual, Ethnicity is Multiracial. Also, she has some hobbies like Voice acting, Modeling, Acting, Singing, Reading And Watching a movie.
In 1978, she was born and grow up with her parents in San Antonio, Texas, United States. His father's name is Rafael Rodriguez (Soldier) and his mother's name is Carmen Milady Pared. She is the only daughter of her parents. Michelle has two brothers like Omar Rodriguez and Raul Rodriguez.
She completed most of the film and she is the most popular face in the American film industry. Her acting career starting at the age of 22. She is the American Actress, Model, Voice actress, and Screenwriter. His Years active 2000–present.
She is the highest-paid and leading American actress and model. Also, she has millions of fans all over the world. His fan/follower Facebook followers are 13.5 million, twitter followers are 1.3 million, and his Youtube followers are 31.8k as of 2019. Rodriguez's Instagram follower is 5.5 million.
Also, Read……..Anne Hathaway Age, Husband, Net Worth, Height, Son, Child, Wiki, Bio.
Michelle Rodriguez's current net worth is $35 million as of 2019. This year she already earned $2 million. His 2018, net worth is $33 million and this year his around estimated earning $3 million. Also, his 2017, net worth is $30 million.
His house address in San Antonio, Texas, United States and this house current value is $5.8 million as of 2019. Also, his salary $40,000 Up to $60,000 Per show or Episode as of 2019.
She has a luxurious car and bike collection like Maruti SX4, Hummer H2, Toyota Corolla, Land Rover Freelander, GMC Sierra, Mitsubishi Pajero SfxBMW x5, Mercedes Benz 5320, Maruti SX4, Hummer H2, Toyota Corolla, etc.
Rodriguez's current boyfriend is Zac Efron (American actor). She recently dated with her boyfriend Zac Efron. Michelle Rodriguez's current relationship status is In a relationship with Zac Efron (American actor).
She had some boyfriends like Vin Diesel (American actor), Colin Farrell (Irish actor), Jesse Jane (American pornographic actress), Lenny Kravitz (American singer-songwriter) and Kristanna Loken (American actress).
Michelle Rodriguez Early life And Career:-
She was born in San Antonio, Texas, United States and she began her carrier as a model at the age of 22 in 2000. Also, she is the daughter of Rafael Rodriguez (Soldier) and Carmen Milady Pared.
She moved with her mother when his age at 8 years in the Dominican Republic and she live here for three years. In the focus, her acting carrier and she completed her goal of becoming a screenwriter and director.
In 2000, the first time she faced audition with her performance as Diana Guzman. Also, she accumulated several awards and nominations for the role in independent circles in 2011.
She completed most of the popular film. Also, his popular film like Girlfight (2000), The Fast and the Furious (2001), Resident Evil (2002), Control (2004), The Breed (2006), Gardens of the Night (2008), Alita: Battle Angel (2019) and Fast & Furious 9 (2020).
He's the most popular television series Slavi's Show (2003), Immortal Grand Prix (2005), CollegeHumor Original (2011), Germany's Next Top Model (2012), Running Wild with Bear Grylls (2015) and Super Into (2015).
Also, Read……..Nicole Kidman Age, Children, Husband, Net Worth, Wiki, Bio.
Michelle Rodriguez's Education qualification is Graduation from General Educational Development. She was studying the name of school William L Dickinson High School. Also, she completed the graduation form of General Educational Development.
She attended most of the awards conference and wins some awards. Also, his awards Chicago Film Critics Association Award (2001), Best Breakthrough Performance by an Actress Award (2001) and Outstanding Performance by an Ensemble in a Drama Series Award (2005).
His favorite food is Tater tots, San Francisco sourdough bread, Pot roast, Fajitas, and Jambalaya. Michelle's favorite actors are Tom Hanks, Robert Downey, Jr., and Denzel Washington. Also, his favorite actress Anne Hathaway, Angelina Jolie, Jodie Foster, and Nicole Kidman.
Mayte Michelle Rodriguez has awesome body measurements like the height in 5 fits 5 inches weight 54 kg. His Waist Size (approx) 25 Inches, Bra Size (approx) 34 Inches and Hip Size (approx) 36 Inches. His eye color is brown – dark, hair color is black and body type average.
Birth Name Mayte Michelle Rodriguez
Nickname Mayte, MRod, The Tough Girl, ToughM, Michelle Rodriguez
Profession(s) Actress, Model, Voice actress, and Screenwriter
Date of Birth July 12, 1978
Blood Group A(+ve)
Hobbies Voice acting, Modeling, Acting, Singing, Reading And Watching movie
Religion Atheist
Zodiac Sign Cancer
Michelle Rodriguez Cars
Cars Maruti SX4, Hummer H2, Toyota Corolla, Land Rover Freelander, GMC Sierra, Mitsubishi Pajero SfxBMW x5, Mercedes Benz 5320, Maruti SX4, Hummer H2, Toyota Corolla, etc.
House San Antonio, Texas, United States
Birthplace San Antonio, Texas, United States
Film Debut Girlfight (2000)
The Fast and the Furious (2001)
Resident Evil (2002)
Gardens of the Night (2008)
Trópico de Sangre (2010)
Resident Evil: Retribution (2012)
Televisions Debuts Slavi's Show (2003)
Immortal Grand Prix (2005)
CollegeHumor Original (2011)
Germany's Next Top Model (2012)
Running Wild with Bear Grylls (2015)
Super Into (2015)
How Old Was She Started Acting? 22 years old
Facebook Facebook Page
Twitter Twitter Account
Instagram Instagram Account
Youtube Youtube Channel
Soundcloud Soundcloud Account
Michelle Rodriguez Current Boyfriend
Boyfriends Vin Diesel (American actor)
Colin Farrell (Irish actor)
Jesse Jane (American pornographic actress)
Olivier Martinez (French film actor)
Lenny Kravitz (American singer-songwriter)
Kristanna Loken (American actress)
Cara Delevingne (Model)
Zac Efron (American actor)
Current Relationship In a relationship with Zac Efron (American actor)
Michelle Rodriguez Family
Father Rafael Rodriguez (Soldier)
Mother Carmen Milady Pared
Brother Omar Rodriguez
Sister Raul Rodriguez
School William L Dickinson High School
University General Educational Development
Qualification Graduation from General Educational Development
Michelle Rodriguez Hot
Favorite Things & Likes
Michelle Rodriguez Favorite Think
Favorite Food Tater tots, San Francisco sourdough bread, Pot roast, Fajitas, and Jambalaya
Favorite Actor Tom Hanks, Robert Downey, Jr., and Denzel Washington
Favorite Actresses Anne Hathaway, Angelina Jolie, Jodie Foster, and Nicole Kidman
Favorite Pets Dog
Favorite Colour White
Favorite Destination San Antonio, Texas, United States
Favourite Car BMW
Favorite Sports Football
Michelle Rodriguez Awards
Awards Chicago Film Critics Association Award (2001)
Outstanding Latino Cast in a Feature Film Award (2001)
Best Breakthrough Performance by an Actress Award (2001)
Outstanding Actor/Actress in a Made for Television Movie or Miniseries Award (2002)
Outstanding Performance by an Ensemble in a Drama Series Award (2005)
TaggedMichelle RodriguezMichelle Rodriguez ageMichelle Rodriguez bioMichelle Rodriguez boyfriendMichelle Rodriguez net worthMichelle Rodriguez sisterMichelle Rodriguez wiki
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Strictly's AJ Pritchard reveals heartbreaking reason he 'jumped at the chance' to take part in Stand Up To Cancer
Naomi Bartram
Strictly Come Dancing star AJ Pritchard has opened up about the sad reason he decided to help raise money for Stand Up To Cancer.
The professional dancer recently took part in Celebrity Hunted for Channel 4 in support of the cause, and has now revealed he agreed to do it because of a moving personal experience.
Speaking about the disease, the star explained that a woman who helped him make it as a professional dancer 'suddenly' died of cancer.
"I would jump at any opportunity to do any work for Stand Up To Cancer," he told Mail Online.
I'm literally so Excited 😆 #CelebrityHunted tomorrow Night 😈 @Hunted_HQ @Channel4 pic.twitter.com/XQ07tAmISp
— AJ Pritchard (@Aj11Ace) October 15, 2018
"If they ask me to do anything it will always be a yes. I do have personal reasons for doing it – the lady that helped fund and allow me to become the dancer I am today passed away so suddenly from cancer."
He then explained, "If it wasn't for her, I wouldn't have been able to travel the world and end up doing Strictly. She allowed me to live my dreams.
"Not everyone can do what I do, and that's down to her… I owe her so much. So my way of giving back to her was to always be thankful and to work my b*****ks off."
THANK YOU** "I Love Dancing 😃🙌🏼 @laurensteadmann " #quickstep
A post shared by A J P R I T C H A R D . (@aj11ace) on Oct 13, 2018 at 11:51am PDT
Unfortunately, AJ's friend passed away before he earned his spot on Strictly Come Dancing in 2016.
"It was very sudden. A complete shock," he said.
"Literally, within two weeks of me last speaking to her, she died from cancer. So I would always take an opportunity like this with both hands.
"She was one of those people that you just say 'that shouldn't have happened to her'. She didn't deserve it."
On The RUN 🏃🏻♂ for @standup2canceruk soon … @hunted_hq @channel4
A post shared by A J P R I T C H A R D . (@aj11ace) on Oct 9, 2018 at 10:13am PDT
AJ – who is currently paired with Paralympian Lauren Steadman on Strictly – will appear on The Hunted for Stand Up To Cancer tonight, alongside former Strictly winner Louis Smith.
And the 23-year-old has said he's 'excited' for people to see the real him.
"It's exciting doing this because everyone sees me on Strictly and its more about my partner," he continued.
"While in this you will get to see my personality more and what makes me tick,' AJ said. 'And who doesn't enjoy a giant game of hide and seek?"


DENVER, Nov 02, 2011 (BUSINESS WIRE) -- Whiting Petroleum Corporation's (NYSE: WLL) production in the third quarter of 2011 totaled a record 6.50 million barrels of oil equivalent (MMBOE), 83% oil/natural gas liquids and 17% natural gas. The daily average production rate was 70,675 barrels of oil equivalent (BOE), an increase of 10% over the 64,120 BOE per day rate in the second quarter of 2011 and a 7% increase over the 66,120 BOE average daily rate in the third quarter of 2010.
James J. Volker, Whiting's Chairman and CEO, commented, "We generated a 10% production increase over the second quarter of 2011 and expect another good increase in the fourth quarter.Our well economics continue to remain robust.From January 2009 through September 30, 2011, we completed 115 Bakken wells with an average first six months production of 97,000 BOE.This average is 13,000 BOE higher than the second ranked Bakken operator and 50,000 BOE better than the average of the next 20 operators.We are currently drilling and completing wells in the Sanish field for approximately $6.0 million.Outside of Sanish, in other North Dakota areas, our completed well costs are currently running between $6.0 and $8.0 million and declining as we move into development mode.Our average lease cost in the Williston Basin is $421 per net acre.We hold 683,000 net acres in the Bakken/Three Forks Hydrocarbon System that we believe will provide increased production and reserve additions for years to come."
(1)Please refer to the definition of De-Risked found later in this release.
The following tables summarize the third quarter and first nine months operating and financial results for 2011 and 2010.
(2) A reconciliation of adjusted net income available to common shareholders to net income available to common shareholders is included later in this news release.
Lewis & Clark/Pronghorn Prospects. Whiting's net production from the Lewis & Clark/Pronghorn prospects averaged 3,960 BOE per day in the third quarter of 2011, up 50% from the 2,640 BOE per day average in the second quarter of 2011. From July 15 through October 22, 2011, Whiting completed 12 new wells at Lewis & Clark/Pronghorn, bringing the total number of producing operated Pronghorn Sand(1) / Three Forks wells to 38. As of October 22, 2011, there were 11 operated wells being completed or awaiting completion and seven wells were being drilled. We currently have seven drilling rigs operating in these prospects, and we expect to have eight rigs working by year-end 2011.
(1) The North Dakota Industrial commission (NDIC) recently renamed the Sanish Sand the Pronghorn Sand.
(2) Excludes the Wolski 44-23H, which was completed in the Scallion formation pumping 188 BOE per day.
(3) Excludes the 5 delineation wells outside the reservoir boundary.
NOTE: A list of all Lewis & Clark/Pronghorn wells completed inception to date is included in our current corporate presentation, which is available on our website.
We own 384,658 gross (255,905 net) acres in the Lewis & Clark/Pronghorn prospects, which is three and a half times the area of Sanish field. Please see the map in our corporate presentation. Based on our drilling activities to date across Lewis & Clark/Pronghorn, we believe that currently 52% of our total acreage is drillable and de-risked (46% at Lewis & Clark and 59% at Pronghorn). The following table summarizes the total and de-risked acreage at Lewis & Clark and Pronghorn.
We believe we can drill two Pronghorn Sand/Three Forks wells per 1,280-acre spacing unit at Lewis & Clark and three Pronghorn Sand/Three Forks wells per 1,280-acre unit at Pronghorn. The following tables show the number of total and de-risked drilling locations at Lewis & Clark/Pronghorn.
Based on production to date at Lewis & Clark/Pronghorn, Hidden Bench, Tarpon, and Cassandra, we believe that our wells on these prospects will have typical Estimated Ultimate Recoveries (EURs) in the 350,000 to 600,000 BOE range.
Hidden Bench Prospect. Whiting's net production from the Hidden Bench prospect averaged 855 BOE per day in the third quarter of 2011. We currently hold 59,734 gross (29,334 net) acres in the prospect, which is located in McKenzie County, North Dakota. Through October 22, 2011, Whiting completed five wells in the Middle Bakken on the prospect. As of October 22, 2011, we had two operated wells waiting on completion and two rigs drilling in the area. Based on our drilling to date, we believe 100% of the prospect is now drillable for the Middle Bakken and de-risked. We estimate we can drill at least two Middle Bakken wells per 1,280-acre spacing unit at Hidden Bench. This equates to 93 gross de-risked Middle Bakken locations. We have not yet tested the Three Forks at Hidden Bench. Please see the Hidden Bench map in our corporate presentation.
Tarpon Prospect. Whiting has set a new initial production record for all Bakken wells drilled in the Williston Basin. The Tarpon Federal 21-4H well was completed in the Middle Bakken (after a 30 stage sliding sleeve frac job) flowing 4,815 barrels of oil and 13,163 Mcf of gas (7,009 BOE) per day on October 17, 2011. The Company owns a 56% working interest and a 45% net revenue interest in the Tarpon well. Whiting drilling engineers also set a new Tarpon Prospect area record by drilling this well to total depth in 13.3 days. We expect the completed well cost to be $6.35 million. Whiting holds 8,125 gross (6,265 net) acres at the Tarpon prospect, which is located in McKenzie County, North Dakota. Whiting has controlling interests in four 1,280-acre Tarpon prospect spacing units. We have the potential to drill a total of 12 Middle Bakken and eight Three Forks wells in these units. Based on our drilling to date, we believe 100% of the prospect is now drillable for the Middle Bakken and de-risked. Please see the Tarpon map in our corporate presentation.
Cassandra Prospect. Whiting currently holds 30,664 gross (14,483 net) acres in Cassandra and has controlling interests in 12 1,280-acre spacing units. We have drilled four wells to date in 2011 with IP's that averaged 878 BOE per day. Based on our drilling and other operators drilling to date, we believe 100% of the prospect is now drillable for the Middle Bakken and de-risked. We have the potential to drill a total of 24 operated Middle Bakken wells and participate in 24 non-operated wells in Cassandra. Please see the Cassandra map in our corporate presentation.
Missouri Breaks Prospect. We plan to spud our first well in Missouri Breaks in the fourth quarter of 2011. Whiting currently holds 58,200 gross (40,250 net) acres in the prospect and has controlling interests in 46 1,280-acre spacing units. Missouri Breaks is located in Richland County, Montana.
(1) Up from 24,350 in the second quarter 2011.
(2) Up from 23,760 in June 2011.
Robinson Lake Gas Plant. As of October 22, 2011, the plant was processing 43.5 MMcf of gas per day (gross). Currently, there is inlet compression in place to process 60 MMcf per day. Compression can be added to 90 MMcf per day as the processing demand increases. Whiting owns a 50% interest in the plant.
Belfield Gas Processing Plant. Whiting continues construction on the 30 MMcf per day gas processing plant located south of Belfield, North Dakota. Steady plant operation is scheduled for January 2012. Gas gathering construction is progressing on schedule. The South Heart Substation, which will supply power to the plant, is currently scheduled to be completed in mid-November 2011. Our Bakken Express project came on-line in late August, allowing us to truck natural gas until the plant and gathering system are completed.
Belfield Oil Terminal. Construction continues on the Belfield oil terminal in our Lewis & Clark prospect area. Construction of a seven mile oil transmission line from Whiting's Belfield oil terminal to the Bridger Pipeline interconnect is expected to start in the fourth quarter of 2011. We estimate that the completion of this terminal will reduce transportation costs by $2.00 - $3.00 per barrel.
Service Availability. During the third quarter of 2011, we had three full-time dedicated frac crews working in the Williston Basin and reduced the number of operated wells waiting on completion from 44 on July 15, 2011 to 26 on October 22, 2011. Based on our current drilling rig count of 18 rigs working in the Williston Basin, a typical inventory of wells waiting on completion is 20 to 25 wells. In anticipation of winter weather delays, we plan to add a fourth frac crew before year-end 2011.
North Ward Estes Field. Production from our North Ward Estes field averaged 8,440 BOE per day in the third quarter of 2011. This average rate represented a 4% increase from the 8,125 net daily rate in the second quarter of 2011.
As we reported in our news release on June 8, 2011, we were experiencing under-deliveries of CO2 contract quantities from our North Ward Estes field CO2 supplier. For most of July 2011, our daily CO2 deliveries were approximately 122 MMcf. Since September 1, 2011, we have been receiving our full contract quantities of 134 MMcf of CO2 per day. Whiting is currently injecting approximately 290 MMcf of CO2 per day into the field, of which about 54% is recycled gas.
Postle Field.In the third quarter of 2011, the Postle field produced at an average net rate of 7,980 BOE per day, a 1% decrease from the 8,095 BOE per day rate in the second quarter 2011. Production rates are expected to be approximately 8,000 BOE per day through year end. Whiting is currently injecting 120 MMcf of CO2 per day into the field, of which approximately 65% is recycled gas.
Delaware Basin:Big Tex Play "Wolfbone".The Wolfbone play consists of a 2,000 to 3,000-foot vertical section of Permian age interbedded carbonate and organic rich siltstone at a depth of 5,700 to 9,500 feet in Pecos, Reeves and Ward counties, Texas. Targets include the Brushy Canyon, Bone Spring, and Wolfcamp horizons. Currently, Whiting has a lease position consisting of 121,771 gross (89,852 net) acres.
Whiting has moved from vertical wells in the Big Tex area to horizontals in this play. We have drilled four horizontals with two rigs currently drilling horizontal wells to test several different objectives. Our first Bone Spring horizontal well, the Bissett 9701H, was drilled with a 3,645-foot lateral and was completed on July 4, 2011 with 18 frac stages. Over the first 30 days, this well averaged 336 BOE per day. We believe this well proved up at least eight development wells, in which we hold a 100% working interest. We have completed two other horizontal wells; the Chilton 5601H fraced with 13 stages in the Bone Spring and the Lizzy 402H fraced with 17 stages in the Brushy Canyon. We are now placing these wells on pump.
Redtail Niobrara Prospect. The Redtail prospect targets the Niobrara "B" zone in the Denver Basin, in Weld County, Colorado. Whiting controls 104,425 gross (76,065 net) acres in the play. The Wild Horse 16-13H, our first well drilled on a northeast azimuth, is currently producing 328 BOE per day. The last well in the initial 2010-2011 five-well program, the Two Mile Creek 22-13H, has been completed with an initial production rate of 216 BOE per day. This well was also drilled on a northeast azimuth.
Based on the results of the Wild Horse and Two Mile Creek wells and utilizing newly acquired 3D, Whiting is currently planning and permitting a second round of drilling. The new program will consist of two north-south oriented 7,000-foot laterals drilled in 960-acre spacing units and two 4,000-foot northeast oriented laterals in 640-acre units. Drill depths within this area range from 5,500 to 6,500 feet with completed well costs of $4.0 million for shorter laterals and $5.5 million for longer laterals. We spudded the first well in late October.
As of October 22, 2011, 27 operated drilling rigs and 63 operated workover rigs were active on our properties. We were also participating in the drilling of four non-operated wells, all in North Dakota.
In the third quarter, we had an average of 12 service units working in our Sanish field. By November 1, 2011, we had increased this number to a total of 26 service units. These service units will be used to reduce the 66 wells in Sanish awaiting service work. The lower number of service units in the third quarter limited our production increase. We expect to reduce the number of shut-in wells awaiting service work in Sanish field to approximately 20 by year-end 2011.
Whiting increased its 2011 capital budget from $1.6 billion to $1.7 billion. The following table provides a breakdown of our budget increase.
temporary sand shortage at a cost of $850K per well.
On October 12, 2011, Whiting entered into an amendment with its banking syndicate to increase the borrowing base under its Credit Agreement from $1.1 billion to $1.5 billion until May 1, 2012, the next redetermination date of the Credit Agreement. No other terms of the Credit Agreement were changed.
(1) Whiting realized pre-tax cash settlement losses of $1.9 million on its crude oil hedges and gains of $0.2 million on its natural gas hedges during the third quarter of 2011. A summary of Whiting's outstanding hedges is included later in this news release.
During the third quarter, the company-wide differential for crude oil/NGLs compared to NYMEX was $8.85 per barrel, which compared to $10.05 per barrel in the second quarter of 2011. We expect our company-wide oil price differential to average between $9.00 and $10.00 during the fourth quarter of 2011. Within the Bakken, Whiting's operated production had a differential of approximately $8.00 per barrel in October 2011.
The company-wide basis differential for natural gas compared to NYMEX in the third quarter was at a premium of $0.80 per Mcf, which compared to a premium of $0.62 per Mcf in the second quarter of 2011. We expect our natural gas to sell at a premium price of between $0.70 and $1.00 during the fourth quarter of 2011.
Co., WY and one non-op development Red River oil test in Richland Co., MT.
nine months ended September 30, 2011, respectively.
The following table provides guidance for the fourth quarter and full-year 2011 based on current forecasts, including Whiting's full-year 2011 capital budget of $1,700.0 million.
reflect the Company's two-for-one stock split in February 2011.
The Company's management will host a conference call with investors, analysts and other interested parties on Thursday, November 3, 2011 at 11:00 a.m. EDT (10:00 a.m. CDT, 9:00 a.m. MDT) to discuss Whiting's third quarter 2011 financial and operating results. Please call (800) 638-5495 (U.S./Canada) or (617) 614-3946 (International) and enter the pass code 36864873 to be connected to the call. Access to a live Internet broadcast will be available at http://www.whiting.com by clicking on the "Investor Relations" box on the menu and then on the link titled "Webcasts." Slides for the conference call will be available on this website beginning at 11:00 a.m. (EDT) on November 3, 2011.
A telephonic replay will be available beginning approximately two hours after the call on Thursday, November 3, 2011 and continuing through Thursday, November 10, 2011. You may access this replay at (888) 286-8010 (U.S./Canada) or (617) 801-6888 (International) and entering the pass code 64578544. You may also access a web archive at http://www.whiting.com beginning approximately one hour after the conference call.
Whiting Petroleum Corporation, a Delaware corporation, is an independent oil and gas company that acquires, exploits, develops and explores for crude oil, natural gas and natural gas liquids primarily in the Permian Basin, Rocky Mountains, Mid-Continent, Gulf Coast and Michigan regions of the United States. The Company's largest projects are in the Bakken and Three Forks plays in North Dakota and its Enhanced Oil Recovery fields in Oklahoma and Texas. The Company trades publicly under the symbol WLL on the New York Stock Exchange. For further information, please visit http://www.whiting.com.
These risks and uncertainties include, but are not limited to: declines in oil or natural gas prices; impacts of the global recession and tight credit markets; our level of success in exploitation, exploration, development and production activities; adverse weather conditions that may negatively impact development or production activities; the timing of our exploration and development expenditures, including our ability to obtain CO2; inaccuracies of our reserve estimates or our assumptions underlying them; revisions to reserve estimates as a result of changes in commodity prices; risks related to our level of indebtedness and periodic redeterminations of the borrowing base under our credit agreement; our ability to generate sufficient cash flows from operations to meet the internally funded portion of our capital expenditures budget; our ability to obtain external capital to finance exploration and development operations and acquisitions; federal and state regulatory initiatives relating to the regulation of hydraulic fracturing; the potential impact of federal debt reduction initiatives and tax reform legislation being considered by the U.S. Federal government that could have a negative effect on the oil and gas industry; our ability to identify and complete acquisitions and to successfully integrate acquired businesses; unforeseen underperformance of or liabilities associated with acquired properties; our ability to successfully complete potential asset dispositions; the impacts of hedging on our results of operations; failure of our properties to yield oil or gas in commercially viable quantities; uninsured or underinsured losses resulting from our oil and gas operations; our inability to access oil and gas markets due to market conditions or operational impediments; the impact and costs of compliance with laws and regulations governing our oil and gas operations; our ability to replace our oil and natural gas reserves; any loss of our senior management or technical personnel; competition in the oil and gas industry in the regions in which we operate; risks arising out of our hedging transactions; and other risks described under the caption "Risk Factors" in our Annual Report on Form 10-K for the period ended December 31, 2010. We assume no obligation, and disclaim any duty, to update the forward-looking statements in this news release.
"De-Risked" core development acreage and related well locations in the Williston Basin refers to acreage and locations that the Company believes the relative geological risks related to recovery have been reduced as a result of drilling operations to date. However, only a small portion of such acreage and locations has been attributed proved undeveloped reserves and ultimate recovery from such acreage and locations remains subject to all the recovery risks applicable to other acreage.
For further information and discussion on the selected financial data below, please refer to Whiting Petroleum Corporation's Quarterly Report on Form 10-Q for the quarter ended September 30, 2011.
one stock split in February 2011.
Company's two-for-one stock split in February 2011.
Adjusted Net Income Available to Common Shareholders is a non-GAAP financial measure.