text:
\section{Introduction}
3D object detection using point clouds has received substantial attention in autonomous vehicles, robotics and augmented/virtual reality. Although the recent development of deep learning has led to the surge of object detection with 2D images \shortcite{ren2015faster,liu2016ssd,redmon2017yolo9000,szegedy2017inception}, it is still non-trivial to apply these 2D methods to 3D point clouds, especially when dealing with the sparsity and unstructured property of point clouds. Besides, the applications usually demand high efficiency from detection systems, making it even harder to design a 3D detector due to the much larger 3D space.

\begin{figure}[!tb]
	\centering {\includegraphics[width=0.47\textwidth]{figs/intro_v1.pdf}}

	\caption{\small Comparison with state-of-the-art 3D object detection methods in two stage paradigm. Instead of aggregating RoI features from a set of point representations, our method directly extract RoI features from 3D voxel feature volumes.
	}
	\label{fig:intro}

\end{figure}

Existing 3D detection methods can be broadly grouped into two categories, \emph{i.e.}, voxel-based and point-based. The \textit{voxel-based} methods \shortcite{zhou2018voxelnet,yan2018second,lang2019pointpillars} divide point clouds into regular grids, which are more applicable for convolutional neural networks (CNNs) and more efficient for feature extraction due to superior memory locality. Nevertheless, the downside is that voxelization  often causes loss of precise position information.
Current state-of-the-art 3D detectors are mainly \textit{point-based}, which take raw point clouds as input, and abstract a set of point representations with iterative sampling and grouping \shortcite{qi2017pointnet,qi2017pointnet++}.  The advanced point-based methods \shortcite{Yang_2019_ICCV,shi2019pointrcnn,Yang_2020_CVPR,Shi_2020_CVPR} are top ranked on various benchmarks \shortcite{geigerwe,caesar2020nuscenes,sun2020scalability}. This has thus led to the popular viewpoint that the precise position information in raw point clouds is crucial for accurate object localization.	Despite the superior detection accuracy, point-based methods are in general less efficient because it is more costly to search nearest neighbor with the point representation for point set abstraction.

As the detection algorithms become mature, we are ready to deploy these algorithms on realistic systems. Here, a new challenge arises: can we devise a method that is as accurate as advanced point-based methods and as fast as voxel-based methods?
In this work, towards this objective, we take the voxel-based framework and try to boost its accuracy. We first argue that precise positioning of  raw point clouds is nice but unnecessary. We observe that voxel-based methods generally perform object detection on the bird-eye-view (BEV) representation, even if the input data are in 3D voxels \shortcite{yan2018second}.
In contrast, point-based methods commonly rely on abstracted point representations to restore 3D structure context and make further refinement based on the point-wise features, as in Figure \ref{fig:intro} (a) (b).
By taking a close look at the underlying mechanisms, we find that the key disadvantage of existing voxel-based methods stems from the fact that they convert 3D feature volumes into BEV representations without ever restoring the 3D structure context.

Bearing this in mind, we propose to aggregate 3D structure context from 3D feature volumes. Specifically, we introduce a novel voxel-based detector, \emph{i.e.}, Voxel R-CNN, to take full advantage of voxel features in a two stage pipeline (see Figure~\ref{fig:intro} (c)). Voxel R-CNN consists of three core modules: (1) a 3D backbone network, (2) a 2D backbone network followed by the Region Proposal Network (RPN), and (3) a detect head with a new voxel RoI pooling operation. The 3D backbone network gradually abstracts voxels into 3D feature volumes. Dense region proposals are generated by the 2D backbone and the RPN. Then, the RoI features are directly extracted from 3D feature volumes with voxel RoI pooling. In designing voxel RoI pooling, we take the neighbor-aware property (which facilitates better memory locality) to extract neighboring voxel features and devise a local feature aggregation module for further acceleration. Finally, the 3D RoI features are taken for further box refinement.

The main contribution of this work stems from the design of Voxel R-CNN, which strikes a careful balance between accuracy and efficiency. The encouraging experiment results of Voxel R-CNN also confirm our viewpoint: the precise positioning of raw points is not essential for high performance 3D object detection and coarser voxel granularity can also offer sufficient spatial context cues for this task. Please note that our Voxel R-CNN framework serves as a simple but effective baseline facilitating further investigation and downstream tasks.

\section{Reflection on 3D Object Detection}

In this section, we first revisit two representative baseline methods, \emph{i.e.}, SECOND \shortcite{yan2018second} and PV-RCNN \shortcite{Shi_2020_CVPR},  and then investigate the key factors of developing a high performance 3D object detector.

\subsection{Revisiting}

\subsubsection{SECOND.} \hskip -5pt  SECOND \shortcite{yan2018second} is a voxel-based one stage object detector.
It feeds the voxelized data to a 3D backbone network for feature extraction. The 3D feature volumes are then converted to BEV representations. Finally, a 2D backbone followed by a Region Proposal Network (RPN) is applied to perform detection.

\subsubsection{PV-RCNN.} \hskip -5pt PV-RCNN \shortcite{Shi_2020_CVPR} extends SECOND by adding a keypoints branch to preserve 3D structural information.
Voxel Set Abstraction (VSA) is introduced to integrate multiscale 3D voxels features into keypoints. The features of each 3D region proposals are further extracted from the keypoints through RoI-grid pooling for box refinement.

\begin{table}[t]
	\small
	\begin{center}
		\scalebox{0.99}{
			\setlength\tabcolsep{6pt}
			\begin{tabular}{l|ccc}
				\hline
				\multirow{2}{*}{\tabincell{c}{Methods}} &
				\multicolumn{3}{c}{$\text{AP}_\text{3D}$ (\%)} \\

				& Easy & Mod.  & Hard\\
				\hline
				PV-RCNN~\shortcite{Shi_2020_CVPR}& 89.35 & 83.69 & 78.70  \\
				\hline
				SECOND~\shortcite{yan2018second} & 88.61 & 78.62 & 77.22 \\
				SECOND +BEV detect head& 89.50 & 79.26 & 78.45\\
				\hline
			\end{tabular}
		}
	\end{center}

	\caption{Performance comparison for adding BEV detect head on the top of SECOND. These results are evaluated on the KITTI \textit{val} set with average precision calculated by 11 recall positions for car class.}
	\label{tab:second_with_bev_head}

\end{table}

\begin{table}[t]
	\centering

	\footnotesize
	\setlength\tabcolsep{3pt}
	\begin{tabular}{|c|c|c|c|c|}
		\hline
		Components & 3D backbone & 2D backbone & VSA & Detect head \\
		\hline
		Avg. time (ms)& 21.1 &  9.4 & 49.4 & 19.1 \\
		\hline
	\end{tabular}

	\caption{Running time comparison for each component in PV-RCNN. This result is calculated by the average over the 3,769 samples in the KITTI \textit{val} set.}
	\label{tab:pvrcnn_timetable}

\end{table}

\subsection{Analysis}
There exists a large gap between SECOND and PV-RCNN in terms of the detection performance (\emph{i.e.}, accuracy and efficiency). These two methods differ in the following ways. Firstly, SECOND is a one stage method while PV-RCNN takes detect head for box refinement. Secondly,  keypoints in PV-RCNN preserve the 3D structure information, while SECOND performs detection directly on BEV representations. To verify the influence of box refinement and 3D structure information on detection performance, we add a detect head on the top of the 2D backbone network in SECOND. Since the BEV boxes are not aligned with the axis, we exploit Rotated RoI Align for RoI feature extraction.

\begin{figure*}[t]
	\centering {\includegraphics[width=0.9\textwidth]{figs/framework_v1.pdf}}

	\caption{An overview of Voxel R-CNN for 3D object detection. The point clouds are first divided into regular voxels and fed into the 3D backbone network for feature extraction. Then, the 3D feture volumes are converted into BEV representation, on which we apply the 2D backbone and RPN for region proposal generation. Subsequently, voxel RoI pooling directly extracts RoI features from the 3D feature volumes. Finally the RoI features are exploited in the detect head for further box refinement.
	}
	\label{fig:framework}

\end{figure*}

As illustrated in Table \ref{tab:second_with_bev_head}, directly adding a BEV detect head on top of BEV features leads to 0.6\% AP improvement  for KITTI car moderate data, but has still trailed the accuracy of PV-RCNN thus far. This verifies the effectiveness of box refinement, and also demonstrates that \textit{the capacity of BEV representation is rather limited}. Typically, PV-RCNN integrates voxel features into sampled keypoints with Voxel Set Abstraction. The keypoints works as an intermediate feature representation to effectively preserve 3D structure information. However, as illustrated in Table \ref{tab:pvrcnn_timetable}, \textit{ the point-voxel interaction takes almost half of the overall running time}, which makes PV-RCNN much slower than SECOND.

\subsubsection{Summary.} \hskip -5pt In summary, by analyzing the limitations of bird-eye-view (BEV) feature representations in SECOND and the computation cost of each component  in PV-RCNN, we observe the following: (a) the \textbf{\textit{3D structure is of significant importance}} for 3D object detectors, since the BEV representation alone is insufficient to precisely predict bounding boxes in a 3D space; and (b) \textbf{\textit{the point-voxel feature interaction is time-consuming}} and affects the detector's efficiency.
These observations motivate us to directly leverage the 3D voxel tensors and develop a voxel-only 3D object detector.

	\section{Voxel R-CNN Design}

	In this section, we present the design of Voxel R-CNN, a voxel-based two stage framework for 3D object detection. As shown in Figure \ref{fig:framework}, Voxel R-CNN includes: (a) a 3D backbone network, (b) a 2D backbone network followed by the Region Proposal Network (RPN), and (c) a voxel RoI pooling and a detect subnet for box refinement.  In Voxel R-CNN, we first divide the raw point cloud into regular voxels and utilize the 3D backbone network for feature extraction. We then convert the sparse 3D voxels into BEV representations, on which we apply the 2D backbone network and the RPN to generate 3D region proposals. Subsequently, we use Voxel RoI pooling to extract RoI features, which are fed into the detect subnet for box refinement. Below we discuss these modules in detail. Since our innovation mainly lies in voxel RoI pooling, we discuss it first.

		\begin{figure}[t]
		\centering {\includegraphics[width=0.45\textwidth]{figs/query.pdf}}

		\caption{ Illustration of ball query and our voxel query (performed in 3D space but shown in 2D space here)
		}
		\label{fig:voxel_query}

	\end{figure}

	\subsection{Voxel RoI pooling}
	To directly aggregate spatial context from 3D voxel feature volumes, we propose voxel RoI pooling.

	\subsubsection{Voxel Volumes as Points.} \hskip -5pt  We represent the sparse 3D volumes as a set of non-empty voxel center points $\{\bm{v}_i=(x_i,y_i,z_i)\}_{i=1}^N$  and their corresponding feature vectors $\{\bm{\phi}_i\}_{i=1}^N$. Specifically, the 3D coordinates of voxel centers are calculated with the indices, voxel sizes and the point cloud boundaries.

	\subsubsection{Voxel Query.} \hskip -5pt We propose a new operation, named voxel query, to find neighbor voxels from the 3D feature volumes.
	Compared to the unordered point clouds, the voxels are regularly arranged in the quantified space, lending itself to easy neighbor access. For example, the 26-neighbor voxels of a query voxel can be easily computed by adding a triplet of offsets $(\Delta_i, \Delta_j, \Delta_k), \Delta_i,\Delta_j,\Delta_k\in\{-1, 0, 1\}$ on the voxel indices $(i, j, k)$. By taking advantage of this property, we devise voxel query to efficiently group voxels. The voxel query is illustrated in Figure \ref{fig:voxel_query}. The query point is first  quantified into a voxel, and then the neighbor voxels can be efficiently obtained by indices translation.
	We exploit Manhattan distance in voxel query and sample up to $K$ voxels within a distance threshold. Specifically, the Manhattan distance $D(\alpha,\beta)$ between the voxel $\alpha=(i_\alpha,j_\alpha,k_\alpha)$ and $\beta=(i_\beta,j_\beta,k_\beta)$ is computed as:
	\begin{equation}\label{equ:distance}
		D_m(\alpha,\beta)=|i_{\alpha} - i_{\beta}| + |j_{\alpha} - j_{\beta}| + |k_{\alpha} - k_{\beta}|.
	\end{equation}

	Suppose there are $N$ non-empty voxels in the 3D feature volumes and we utilize ball query to find neighboring voxels to a given query point, the time complexity is $O(N)$. Nevertheless, the time complexity of conducting voxel query is only $O(K)$, where K is number of neighbors. The neighbor-aware property makes it more efficient to group neighbor voxel features with our voxel query than to group neighbor point features with ball query \shortcite{qi2017pointnet++}.

	\subsubsection{Voxel RoI Pooling Layer.} \hskip -5pt
	We design the voxel RoI pooling layer as follows. It starts by dividing a region proposal into $G\times G\times G$ regular sub-voxels. The center point is taken as the grid point of the corresponding sub-voxel. Since 3D feature volumes are extremely sparse (non-empty voxels account for $<3\%$ spaces), we cannot directly utilize max pooling over features of each sub-voxel as in \shortcite{girshick2015fast}. Instead, we integrate features from neighboring voxels into the grid points for feature extraction. Specifically, given a grid point $\bm{g}_i$, we first exploit voxel query to group a set of neighboring voxels $\Gamma_i=\{\bm{v}_i^1, \bm{v}_i^2,\cdots,\bm{v}_i^K\}$. Then, we aggregate the neighboring voxel features with a PointNet module \shortcite{qi2017pointnet} as:
	\begin{equation}\label{equ:mlpmaxpool}

		\bm{\eta}_i=\max \limits_{k=1,2,\cdots,K}\{\Psi([\bm{v}_i^k-\bm{g}_i;\bm{\phi}_i^k])\},
	\end{equation}
	where $\bm{v}_i - \bm{g}_i$ represents the relative coordinates, $\bm{\phi}_i^k$ is the voxel feature of $\bm{v}_i^k$, and $\Psi(\cdot)$ indicates an MLP. The max pooling operation $\max(\cdot)$ is performed along the channels to obtain the aggregated feature vector $\bm{\eta}_i$. Particularly, we exploit Voxel RoI pooling to extract voxel features from the 3D feature volumes out of the last two stages in the 3D backbone network. And for each stage, two Manhattan distance thresholds are set to group voxels with multiple scales. Then, we concatenate the aggregated features pooled from different stages and scales to obtain the RoI features.

		\begin{figure}[t]
		\centering {\includegraphics[width=0.48\textwidth]{figs/simple_mlp.pdf}}

		\caption{Illustration of different schemes to aggregate voxel features: (a) original PointNet module; (b) accelerated PointNet module. We only pick one grid point as an example, and the same operations are applied on all the other grid points.}
		\label{fig:mlp}

	\end{figure}

	\subsubsection{Accelerated Local Aggregation.} \hskip -5pt
	Even with our proposed voxel query,
	the local aggregation operation (\emph{i.e.}, PointNet module) in Voxel RoI pooling still involves large computation complexity.
	As shown in Figure \ref{fig:mlp} (a), there are totally $M$ grid points ($M=r\times G^3$, where $r$ is the number of RoI, and $G$ is the grid size), and $K$ voxels are grouped for each grid point. The dimension of grouped feature vectors is $C+3$, including the C-dim voxel features and 3-dim relative coordinates.
	The grouped voxels occupy a lot of memories and lead to large computation FLOPs ($O(M\times K \times(C+3)\times C')$) when applying the FC layer.

	As motivated by \shortcite{liu2020closer, hu2020randla}, we additionally introduce an accelerated PointNet Module to further reduce the computation complexity of Voxel Query. Typically, as shown in Figure \ref{fig:mlp} (b), the voxel features and relative coordinates are decomposed into two streams.  Given the FC layer with weight $W\in\mathds{R}^{C',C+3}$, we divide it into $W_\text{F}\in\mathds{R}^{C',C}$ and $W_\text{C}\in\mathds{R}^{C',3}$. Since the voxel features are independent of the grid points, we apply a FC layer with $W_\text{F}$ on the voxel features before performing voxel query. Then, after voxel query, we only multiply the grouped relative coordinates by $W_\text{C}$ to obtain the relative position features and add them to the grouped voxel features. The FLOPs of our accelerated PointNet module are $O(N\times C \times C'+M\times K\times 3\times C')$. Since the number of grouped voxels $(M\times K)$ is an order of magnitude higher than $N$, the accelerated PointNet module is more efficient than the original one.

	\subsection{Backbone and Region Proposal Networks}
	We follow the similar design of \shortcite{yan2018second,He_2020_CVPR,Shi_2020_CVPR} to build our backbone networks. The 3D backbone network gradually converts the voxelized inputs into feature volumes.

	Then, the output tensors are stacked along the Z axis to produce BEV feature maps. The 2D backbone network consists of two components: a top-down feature extraction sub-network with two blocks of standard $3\times3$ convolution layers and a mutli-scale feature fusion sub-network that upsamples and concatenates the top-down features. Finally, the output of the 2D backbone network is convolved with two sibling $1\times1$ convolutional layers to generate 3D region proposals. We will detail the architecture of our backbone networks in Section 4.2.

	\subsection{Detect Head}
	The detect head takes RoI features as input for box refinement. Specifically,

	a shared 2-layer MLP first transform RoI features into feature vectors. Then, the flattened features are injected into two sibling branches: one for bounding box regression and the other for confidence prediction. As motivated by \shortcite{jiang2018acquisition,li2019gs3d,Shi_2020_CVPR,shi2020points}, the box regression branch predicts the residue from 3D region proposals to the ground truth boxes, and the confidence branch predicts the IoU-related confidence score.

	\subsection{Training Objectives}

	\subsubsection{Losses of RPN.} \hskip -5pt We follow \shortcite{yan2018second,lang2019pointpillars} to devise the losses of the RPN as a combination of classification loss and box regression loss, as:
	\begin{equation}\label{equ.loss_rpn}\small
		\mathcal{L}_\text{RPN} = \frac{1}{N_{\text{fg}}}[\sum_{i} \mathcal{L}_{\text{cls}}(p_i^a, c_i^{\ast})
		+\mathds{1}(c_i^{\ast}\ge{1})\sum_{i} \mathcal{L}_{\text{reg}}(\delta_i^a, t_i^{\ast})],
	\end{equation}
	where $N_\text{fg}$ represents the number of foreground anchors, $p_i^a$ and $\delta_i^a$ are the outputs of classification and box regression branches, $c_i^{\ast}$ and $t_i^{\ast}$ are the classification label and regression targets respectively. $\mathds{1}(c_i^{\ast}\ge{1})$ indicates regression loss in only calculated with foreground anchors. Here we utilize Focal Loss \shortcite{lin2017focal} for classification and Huber Loss for box regression.

	\subsubsection{Losses of detect head.} \hskip -5pt The target assigned to the confidence branch is an IoU related value, as:
	\begin{equation}
		l_i^\ast(\text{IoU}_i)=\left\{
		\begin{array}{lll}
			0 & & {\text{IoU}_i < \theta_L},\\
			\frac{\text{IoU}_i - \theta_L}{\theta_H-\theta_L} & & {\theta_L \leq \text{IoU}_i < \theta_H},\\
			1 & & {\text{IoU}_i > \theta_H},
		\end{array} \right.
	\end{equation}
	where $\text{IoU}_i$ is the IoU between the $i$-th proposal and the corresponding ground truth box, $\theta_H$ and $\theta_L$ are foreground and background IoU thresholds. Binary Cross Entropy Loss is exploited here for confidence prediction. The box regression branch also uses Huber Loss as in the RPN. The losses of our detect head are computed as:
	\begin{equation}\label{equ.loss_head}\small
		\begin{split}
			\mathcal{L}_\text{head} = &\frac{1}{N_s}[\sum_{i} \mathcal{L}_{\text{cls}}(p_i,l_i^\ast(\text{IoU}_i))\\
			&+\mathds{1}(\text{IoU}_i\ge{\theta_{reg}})\sum_{i} \mathcal{L}_{\text{reg}}(\delta_i, t_i^{\ast})],
		\end{split}

	\end{equation}
	where $N_s$ is the number of sampled region proposals at the training stage, and $\mathds{1}(\text{IoU}_i\ge{\theta_{reg}})$ indicates that only region proposals with $\text{IoU} > \theta_\text{reg}$ contribute to the regression loss.

	\section{Experiments}

	\subsection{Datasets}

	\subsubsection{KITTI.} \hskip -5pt
	The \textit{KITTI Dataset} \shortcite{geigerwe} contains 7481 training samples and 7518 testing samples in autonomous driving scenes. As a common practice, the training data are divided into a \textit{train} set with $3712$ samples and a \textit{val} set with $3769$ samples.  The performance on both the \textit{val} set and online test leaderboard are reported for comparison. When performing experimental studies on the \textit{val} set, we use the \textit{train} set for training. For test server submission, we randomly select $80\%$ samples from the training point clouds for training, and use the remaining $20\%$ samples for validation as in \shortcite{Shi_2020_CVPR}.

	\subsubsection{Waymo Open Dataset.} \hskip -5pt
	The \textit{Waymo Open Dataset} \shortcite{sun2020scalability} is the largest public dataset for autonomous driving to date. There are totally $1,000$ sequences in this dataset, including $798$ sequences ($\sim$$158\text{k}$ point clouds samples) in training set and 202 sequences ($\sim$$40\text{k}$ point clouds samples) in validation set. Different from KITTI that only provides annotations in camera FOV, the
	\textit{Waymo Open Dataset} provides annotations for objects in the full $360^\circ$.

	\subsection{Implementation Details}

	\subsubsection{Voxelization.} \hskip -5pt
	The raw point clouds are divided into regular voxels before taken as the input of our Voxel R-CNN. Since the KITTI Dataset only provides the annotations of object in FOV, we clip the range of point clouds into $[0, 70.4]m$ for the $X$ axis, $[-40,40]m$ for the $Y$ axis and $[-3,1]m$ for Z axis. The input voxel size is set as $(0.05m, 0.05m, 0.1m)$. For the Waymo Open Dataset, the range of point clouds is clipped into $[-75.2, 75.2]m$ for X and Y axes and $[-2, 4]m$ for the Z axis. The input voxel size is set as $(0.1m, 0.1m, 0.15m)$.

	\subsubsection{Network Architecture.} \hskip -5pt
	The architecture of 3D backbone and 2D backbone follows the design in \cite{yan2018second,Shi_2020_CVPR}. There are four stages in the 3D backbone with filter numbers of $16, 32, 48, 64$ respectively. There are two blocks in the 2D backbone network, the first block keeps the same resolution along $X$ and $Y$ axes as the output of 3D backbone network, while the second block is half the resolution of the first one. The numbers of convolutional layers in these two blocks, \emph{i.e.}, $N1$ and $N2$, are both set as 5. And the feature dimensions of these blocks are $(64, 128)$ for KITTI Dataset and $(128, 256)$ for Waymo Open Dataset.
	Each region proposal is divided into a $6\times 6\times 6$ grid for Voxel RoI pooling. The Manhattan distance thresholds are set as $2$ and $4$ for multi-scale voxel query. The output channels of the local feature aggregation module are $32$ for KITTI Dataset and $64$ for Waymo Open Dataset.

		\begin{table}
		\small
		\renewcommand\arraystretch{1.05}

		\begin{center}
			\scalebox{0.8}[0.8]{
				\setlength\tabcolsep{8pt}
				\begin{tabular}{c|c|ccc}
					\hline
					\multirow{2}{*}{Method} &
					\multicolumn{1}{c|}{FPS} &
					\multicolumn{3}{c}{$\text{AP}_\text{3D}$ (\%) ~~} \\
					&(Hz)&Easy & Mod. & Hard \\
					\hline
					\multicolumn{5}{c}{RGB+LiDAR} \\
					\hline
					MV3D \shortcite{chen2017multi} & - & 74.97 & 63.63 & 54.00 \\

					AVOD-FPN \shortcite{ku2018joint} &  10.0 &  83.07 & 71.76 & 65.73 \\
					F-PointNet \shortcite{qi2018frustum} &  5.9 & 82.19 & 69.79 & 60.59 \\
					PointSIFT+SENet \shortcite{zhao20193d} &  - & 85.99 & 72.72 & 64.58 \\
					UberATG-MMF \shortcite{liang2019multi} & - & 88.40 & 77.43 & 70.22 \\
					\hline
					\multicolumn{5}{c}{LiDAR-only} \\
					\hline
					\textbf{Point-based:} &  &  &  &  \\
					PointRCNN \shortcite{shi2019pointrcnn} &10.0 & 86.96 & 75.64 & 70.70  \\
					STD \shortcite{Yang_2019_ICCV} &12.5 &  87.95 & 79.71 & 75.09\\
					Patches \shortcite{lehner2019patch} &- &  88.67 & 77.20 & 71.82 \\
					3DSSD \shortcite{Yang_2020_CVPR} &  26.3 &88.36 & 79.57 & 74.55\\
					PV-RCNN \shortcite{Shi_2020_CVPR} &8.9 &  90.25 & 81.43 & 76.82 \\
					\textbf{Voxel-based:} &  &  &  &  \\
					VoxelNet \shortcite{zhou2018voxelnet} & - & 77.47 & 65.11 & 57.73 \\
					SECOND \shortcite{yan2018second} &   30.4 & 83.34 & 72.55 & 65.82 \\
					PointPillars \shortcite{lang2019pointpillars} &  42.4 & 82.58 & 74.31 & 68.99 \\
					Part-$A^2$ \shortcite{shi2020points} & - & 87.81 & 78.49 & 73.51 \\
					TANet \shortcite{liu2020tanet} &  28.7 & 85.94 & 75.76 & 68.32 \\
					HVNet \shortcite{Ye_2020_CVPR} & 31.0 &  87.21 & 77.58 & 71.79 \\
					SA-SSD \shortcite{He_2020_CVPR} & 25.0 &  88.75 & 79.79 & 74.16\\
					\hline\hline
					Voxel R-CNN (ours) & 25.2 & \textbf{90.90} & \textbf{81.62} & \textbf{77.06} \\
					\hline
				\end{tabular}
			}
		\end{center}

		\caption{Performance comparison on the KITTI \textit{test} set with AP calculated by recall 40 positions for car class}
		\label{tab:test}

	\end{table}

	\subsubsection{Training.} \hskip -5pt
	The whole architecture of our Voxel R-CNN is end-to-end optimized with the Adam optimizer.  For KITTI Dataset, the network is trained for 80 epochs with the batch size 16. For Waymo Open Dataset, the network is trained for 30 epochs with the batch size 32. The learning rate is initialized as $0.01$ for both datasets and updated by cosine annealing strategy.
	In the detect head, the foreground IoU threshold $\theta_\text{H}$ is set as 0.75,  background IoU threshold $\theta_\text{L}$ is set as 0.25, and the box regression IoU threshold $\theta_\text{reg}$ is set as 0.55. We randomly sample $128$ RoIs as the training samples of detect head. Within the sampled RoIs, half of them are positive samples that have IoU $>$ $\theta_\text{reg}$ with the corresponding ground truth boxes. We conduct data augmentation at the training stage following

	strategies in \shortcite{lang2019pointpillars,Shi_2020_CVPR,Yang_2020_CVPR,Ye_2020_CVPR}. Please refer to OpenPCDet \shortcite{openpcdet2020} for more detailed configurations since we conduct all experiments with this toolbox.

	\subsubsection{Inference.} \hskip -5pt
	At the inference stage, we first perform non-maximum suppression (NMS) in the RPN with IoU threshold $0.7$ and keep the top-100 region proposals as the input of detect head. Then, after refinement, NMS is applied again with IoU threshold $0.1$ to remove the redundant predictions.

	\subsection{Results on KITTI Dataset}
	We evaluate our Voxel R-CNN on KITTI Dataset following the common protocol to report the average precision (AP) of class Car with the 0.7 (IoU) threshold. We report our performance on both $val$ set and $test$ set for comparison and analysis. The performance on \textit{val} set is calculated with the AP setting of recall 11 positions. And the results evaluated by the test server utilize AP setting of recall 40 positions$\footnote{The setting of AP calculation is modified from recall 11 positions to recall 40 positions on 08.10.2019. We exploits the recall 11 setting on $val$ set for fair comparison with previous methods.}$ .

	\begin{table}[!t]
	\small
	\begin{center}
		\scalebox{0.99}[0.9]{
			\setlength\tabcolsep{4.2pt}
			\begin{tabular}{c|ccc|ccc}
				\hline
				\multirow{2}{*}{\tabincell{c}{IoU \\ Thresh.}} &
				\multicolumn{3}{c|}{$\text{AP}_\text{3D}$ (\%)} &
				\multicolumn{3}{c}{$\text{AP}_\text{BEV}$ (\%)}\\
				& Easy & Moderate  & Hard & Easy & Moderate  & Hard \\
				\hline
				0.7 & 92.38 & 85.29 & 82.86 & 95.52 & 91.25 & 88.99 \\
				\hline
			\end{tabular}
		}
	\end{center}

	\caption{Performance of Voxel R-CNN on the KITTI \emph{val}  set with AP calculated by 40 recall positions for car class}
	\label{tab:val_r40}

\end{table}

	\begin{table}[!t]
		\small
		\renewcommand\arraystretch{1}

		\begin{center}
			\scalebox{0.75}[0.75]{
				\setlength\tabcolsep{11pt}
				\begin{tabular}{c|ccc}
					\hline
					\multirow{2}{*}{Method} &
					\multicolumn{3}{c}{$\text{AP}_\text{3D}$ (\%) ~~} \\
					&Easy & Moderate & Hard \\
					\hline
					\textbf{Point-based:} & &  &   \\
					PointRCNN \shortcite{shi2019pointrcnn} & 88.88 &78.63& 77.38  \\
					STD \shortcite{Yang_2019_ICCV}  &  89.70 & 79.80 & \textbf{79.30}\\

					3DSSD \shortcite{Yang_2020_CVPR}  &89.71 & 79.45 & 78.67\\
					PV-RCNN \shortcite{Shi_2020_CVPR} &  89.35 & 83.69 & 78.70 \\
					\textbf{Voxel-based:} & &  &   \\
					VoxelNet \shortcite{zhou2018voxelnet} & 81.97 & 65.46 & 62.85 \\
					SECOND \shortcite{yan2018second}  & 88.61 & 78.62 & 77.22 \\
					PointPillars \shortcite{lang2019pointpillars} &86.62 &76.06 &68.91  \\
					Part-$A^2$ \shortcite{shi2020points} & 89.47 & 79.47 & 78.54 \\
					TANet \shortcite{liu2020tanet}  & 87.52 & 76.64 & 73.86 \\

					SA-SSD \shortcite{He_2020_CVPR}  &  \textbf{90.15} & 79.91 & 78.78\\
					\hline\hline
					Voxel R-CNN (ours)  & 89.41 & \textbf{84.52} & 78.93 \\
					\hline
				\end{tabular}
			}
		\end{center}

		\caption{Performance comparison on the KITTI \textit{val} set with AP calculated by 11 recall positions for car class
		}
		\label{tab:val_r11}

	\end{table}

	\subsubsection{Comparison with State-of-the-art Methods.} \hskip -5pt
	We compare our Voxel R-CNN with several state-of-the-art methods on the KITTI $test$ set by submitting our results to the online test server. As shown in the Table \ref{tab:test}, our Voxel R-CNN makes the best balance between the accuracy and efficiency among all the methods. By taking full advantage of voxel-based representation, Voxel R-CNN achieves $81.62\%$ average precision (AP) on the moderate level of class Car, with the real time processing frame rate ($25.2$ FPS). Particularly, Voxel R-CNN achieves comparable accuracy with the strongest competitor, \emph{i.e.}, PV-RCNN \shortcite{Shi_2020_CVPR}, with only about $1/3$ running time. This verifies the  spatial  context  in  the  voxel  representation  is  almost sufficient for 3D object detection, and the voxel representation is more efficient for feature extraction. Besides, Voxel R-CNN outperforms all of the existing voxel-based models by a large margin, \emph{i.e.}, $2.15\%, 1.83\%, 2.90\%$ absolute improvements for easy, moderate and hard level over the SA-SSD\shortcite{He_2020_CVPR}.

	The AP for 3D object detection and BEV object detection of our Voxel R-CNN on KITTI Dataset is presented in Table \ref{tab:val_r40}. The results in this table are calculated by recall 40 positions with the (IoU) threshold $0.7$. In addition, we report the performance on the KITTI $val$ set with AP calculated by recall 11 positions for further comparison. As shown in Table \ref{tab:val_r11}, our Voxel R-CNN achieves the best performance on moderate and hard level on the $val$ set.

	Overall, the results on both $val$ set and $test$ set consistently demonstrate that our proposed Voxel R-CNN achieves the state-of-the-art average precision on 3D object detection and keeps the high efficiency of voxel-based models.

\begin{table}
	\small

	\begin{center}
		\scalebox{0.72}[0.72]{
			\setlength\tabcolsep{5pt}
			\begin{tabular}{c|cccc}
				\hline
				Method
				& Overall & 0-30m & 30-50m & 50m-Inf \\
				\hline
				\textbf{\textit{LEVEL\_1 3D mAP (IoU=0.7):}} & &  & \\
				PointPillar \shortcite{lang2019pointpillars} & 56.62 & 81.01 & 51.75 & 27.94  \\
				MVF \shortcite{zhou2020end} & 62.93 & 86.30 & 60.02 & 36.02 \\
				Pillar-OD \shortcite{wang2020pillar} & 69.80 & 88.53 & 66.50 & 42.93 \\
				AFDet+PointPillars-0.10 \shortcite{ge2020afdet} & 63.69 & 87.38 & 62.19 & 29.27 \\
				PV-RCNN \shortcite{Shi_2020_CVPR}& {70.30} & {91.92} & {69.21} & {42.17} \\
				\textbf{Voxel R-CNN (ours)} &\textbf{75.59}& \textbf{92.49}&\textbf{74.09}&\textbf{53.15} \\
				\hline
				\textbf{\textit{LEVEL\_1 BEV mAP (IoU=0.7):}} & &  &   \\
				PointPillar \shortcite{lang2019pointpillars} & 75.57 & 92.1 & 74.06 & 55.47 \\
				MVF \shortcite{zhou2020end} & 80.40 & 93.59 & 79.21 & 63.09 \\
				Pillar-OD \shortcite{wang2020pillar} & 87.11 & 95.78 & 84.87 & 72.12 \\
				PV-RCNN \shortcite{Shi_2020_CVPR}& {82.96} & {97.35} & {82.99} & {64.97} \\
				\textbf{Voxel R-CNN (ours)} & \textbf{88.19} & \textbf{97.62} & \textbf{87.34} & \textbf{77.70}  \\
				\hline
				\textbf{\textit{LEVEL\_2 3D mAP (IoU=0.7):}} & &  & \\
				PV-RCNN \shortcite{Shi_2020_CVPR}& 65.36 & 91.58 & 65.13 & 36.46 \\
				\textbf{Voxel R-CNN (ours)} &\textbf{66.59} & \textbf{91.74} & \textbf{67.89} & \textbf{40.80} \\

				\hline
				\textbf{\textit{LEVEL\_2 BEV mAP (IoU=0.7):}} & &  & \\
				PV-RCNN \shortcite{Shi_2020_CVPR}& 77.45 & 94.64 & 80.39 & 55.39 \\
				\textbf{Voxel R-CNN (ours)} &  \textbf{81.07} & \textbf{96.99} & \textbf{81.37} & \textbf{63.26} \\
				\hline
			\end{tabular}
		}
	\end{center}

	\caption{Performance comparison on the Waymo Open Dataset with 202 validation sequences ($\sim$40k samples) for the vehicle detection
	}
	\label{tab:waymo}

\end{table}

	\subsection{Results on Waymo Open Dataset}
	We also conduct experiments on the larger Waymo Open Dataset to further validate the effectiveness of our proposed Voxel R-CNN. The objects on the Waymo Open Dataset are split into two levels based on the number of points of a single object, where the LEVEL\_1 objects have more than 5 points while the LEVEL\_2 objects have 1$\sim$5 points.

	\subsubsection{Comparison with State-of-the-art Methods.} \hskip -5pt
	We evaluate our Voxel R-CNN on both LEVEL\_1 and LEVEL\_2 objects and compare with several top-performing methods on the Waymo Open Dataset. Table~\ref{tab:waymo} shows that our method surpasses all previous methods with remarkable margins on all ranges of both LEVEL\_1 and LEVEL\_2. Specifically, with the commonly used LEVEL\_1 3D mAP evaluation metric, our Voxel R-CNN achieves new state-of-the-art performance with 75.59\% mAP, and outperforms previous state-of-the-art method PV-RCNN by 5.29\% mAP. Especially, our method outperforms PV-RCNN with +10.98\% mAP in the range of 50m-Inf, the significant gains on the far away area demonstrate the effectiveness of our proposed Voxel R-CNN for detecting the objects with very sparse points.

	\subsection{Ablation Study}

	Table \ref{tab:ablation} details how each proposed module influences the accuracy and efficiency of our Voxel R-CNN. The results are evaluated with AP of moderate level for car class.

	\textit{Method (a)} is the one stage baseline that performs detection on BEV features. It runs at 40.8 FPS, but with unsatisfactory AP, which indicates \textit{BEV representation alone is insufficient to precisely detect objects in a 3D space}.

	\textit{Method (b)} extends \textit{(a)} with a detect head for box refinement, which leads to a boost of $4.22\%$ moderate AP. This verifies \textit{the spatial context from 3D voxels provides sufficient cues for precise object detection}. However, \textit{(b)} applies time-consuming ball query and original PointNet modules to extract RoI features, leading to a decrease of 23.4 FPS.

	\textit{Method (c)} replaces ball query with our voxel query, which makes 1.7 FPS improvement by \textit{taking advantage of the neighbor-aware property of voxel-based representations}.

	\textit{Method (d)} uses our accelerated PointNet module to aggregate voxel features, boosting the FPS from $17.4$ to $21.4$.

	\textit{Method (e)} is the proposed Voxel R-CNN. By extending the one stage baseline with a detect head, taking voxel query for voxel grouping, and utilizing the accelerated PointNet module for local feature aggregation, Voxel R-CNN \textit{achieves the state-of-the-art accuracy} for 3D object detection and \textit{maintain the high efficiency} of voxel-based models.

	\begin{table}
	\renewcommand\arraystretch{1}
	\small

	\begin{center}
		\scalebox{0.9}[0.9]{
			\setlength\tabcolsep{6pt}
			\begin{tabular}{c|ccc|c|c}
				\hline
				Methods &D.H. & V.Q.  & A.P. & Moderate $\text{AP}_\text{3D}$ (\%)& FPS (Hz) \\
				\hline
				(a) & &  &    & 77.99  & 40.8 \\
				(b) &  $\bm{\surd}$   &  &   & 84.21  & 17.4 \\
				(c) &  $\bm{\surd}$  & $\bm{\surd}$  &  & 84.33  &  19.1\\
				(d) & $\bm{\surd}$   &  & $\bm{\surd}$  & 84.27  & 21.4  \\
				(e) & $\bm{\surd}$   & $\bm{\surd}$& $\bm{\surd}$  & 84.52 & 25.2 \\
				\hline
			\end{tabular}
		}
	\end{center}

	\caption{Performance of proposed method with different configurations on KITTI \textit{val} set. ``D.H.'',  ``V.Q.'' and ``A.P.'' stand for detect head, voxel query and accelerated PointNet module respectively. The results are evaluated with the average precision calculated by 11 recall positions for car class.
	}
	\label{tab:ablation}

\end{table}

	\section{Related Work}

	We broadly categorize the methods for 3D object detection on point clouds into point-based \shortcite{shi2019pointrcnn,Yang_2019_ICCV,Chen2019fastpointrcnn,Yang_2020_CVPR,Shi_2020_CVPR} methods and voxel-based \shortcite{zhou2018voxelnet,yan2018second,yang2018pixor,simon2018complex,lang2019pointpillars,liu2020tanet} methods. Here we give a brief review on these two directions:

	\textbf{Point-based Methods.}  Point-based methods take the raw point clouds as inputs, and abstract a set of point representations with iterative sampling and grouping \shortcite{qi2017pointnet,qi2017pointnet++}.

	PointRCNN \shortcite{shi2019pointrcnn}  introduces a 3D region proposal network based on PointNet++ \shortcite{qi2017pointnet++} and utilizes point cloud RoI pooling to extract 3D region features of each proposal. STD \shortcite{Yang_2019_ICCV} exploits the PointsPool operation to extract points features of each proposal and transfer the sparse points into dense voxel representation. Then, 3D CNNs are applied on the voxelized region features for further refinement. 3DSSD \shortcite{Yang_2020_CVPR} introduces F-FPS as a supplement of D-FPS and builds a one stage anchor-free 3D object detector based on feasible representative points. Different from grouping neighboring points for set abstraction, PV-RCNN \shortcite{Shi_2020_CVPR} devises voxel set abstraction to integrates multi-scale voxel features into sampled keypoints.  By leveraging both the accurate position information from raw points and spatial context from voxel representations, PV-RCNN achieves  remarkable improvements on 3D object detection.

	\textbf{Voxel-based Methods.} Voxel-based methods typically discrete point clouds into equally spaced grids, and then capitalize on 2D/3D CNN to perform object detection. The early work VoxelNet \shortcite{zhou2018voxelnet} first divides points into 3D voxels and uses a tiny PointNet to transform points of each voxel into a compact feature representation. Then, 3D CNNs are leveraged to aggregate spatial context and generate 3D detections. However, due to the sparsity of non-empty voxels in the large space, it is inefficient to exploit conventional convolution networks for feature extraction.

	To reduce the computation cost,  SECOND \shortcite{yan2018second} introduces sparse 3D convolution for efficient voxel processing. SA-SSD \shortcite{He_2020_CVPR} proposes an auxiliary network and losses on the basis of SECOND \shortcite{yan2018second} to preserve structure information. Different from the methods operating on voxels in 3D space,

	PointPillars \shortcite{lang2019pointpillars} groups points as ``pillars'' and capitalizes on a simplified PointNet \shortcite{qi2017pointnet,qi2017pointnet++} for pillar feature extraction before forming pseudo BEV images.

	\section{Conclusion}
	In this paper, we present Voxel R-CNN, a novel 3D object detector with voxel-based representations. Taking the voxels as input, Voxel R-CNN first generates dense region proposals from bird-eye-view feature representations, and subsequently, utilizes voxel RoI pooling to extract region features from 3D voxel features for further refinement. By taking full advantage of voxel representations, our Voxel R-CNN strikes a careful balance between accuracy and efficiency. The encouraging results on both KITTI Dataset and Waymo Open Dataset demonstrate our Voxel-RCNN can serve as a simple but effective baseline to facilitate the investigation of 3D object detection and other downstream tasks.

	\section{Acknowledgements}
	This work was supported by the National Major Program for Technological Innovation 2030—New Generation Artificial Intelligence (2018AAA0100500). The work of Wengang Zhou was supported in part by NSFC under Contract 61822208, 61632019 and 62021001, and in part by the Youth Innovation Promotion Association CAS under Grant 2018497. The work of Houqiang Li was supported by NSFC under Contract 61836011.

	\newpage
	\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


The recess week in the middle of each semester of the academic calendar is to allow students to take a break after 6 weeks of classes. While students may use this week to revise and consolidate their learning in all their modules, they should also be able to relax and re-charge after 6 weeks of intense academic study.
Therefore, please note that, barring unforeseen circumstances, faculty should not make assignments due or schedule make-up classes during Recess Week.


For the second year in a row, Donovan Group NZ Ltd (Coresteel's parent company) has been named one of New Zealand's top companies in the engineering industry, as listed by the NZ Engineering News 2017 Annual Directory.
Company director Brett Donovan was also named within the directory's list of the 50 Who's Who within the engineering and manufacturing industries.
The company was chosen by a selected panel of industry experts on the basis of creativity, innovation, and standout contribution to engineering. Among others, the panel included New Zealand Manufacturers and Exporters Association (NZMEA) chief executive Dieter Adam, New Zealand Heavy Engineering Research Association (HERA) director Wolfgang Scholz, and Wellington Institute of Technology (WelTec) senior executive officer Caryn Ellis.
Donovan Group was featured alongside a number of industry leaders, such as Beca, Holmes Consulting Group, OJI Fibre Solutions, and Fisher & Paykel Healthcare.
Director of Donovan Group Brett Donovan was listed in the '50 Who's Who' directory for his enduring contribution to engineering.
Read the profiles from the NZ Engineering News Annual Directory.


After a valiant fight with an ongoing illness, Martin Alvis "Abbott" Fogleman, 77, of Snow Camp passed away on Thursday, March 9, 2017 at Alamance Regional Medical Center, surrounded by loved ones.
Martin retired from Carolina Tank Lines as Operations Manager and was an active member of Mt. Zion Baptist Church where he served as a deacon.
A celebration of life honoring Martin will be held on Saturday, March 11, 2017 at 2:00 p.m. at Mt. Zion Baptist Church with Rev. Andy Clapp officiating; burial will follow in the church cemetery. The family will receive friends one hour prior to the service in the church sanctuary.
He was the son of the late Rev. Martin Lester Fogleman and Bessie Loraine Hornaday Fogleman, and was also preceded in death by his step-mother Nannie York Cheek Fogleman and his sister, Nancy Fogleman Jones.
Beloved husband to Barbara Ann Fogleman of 58 years and loving father to daughters, Dana Fogleman Sheffield and Jill Fogleman Oakley and husband, Bruce; four grandchildren, Shannon Hall Shropshire, Ashley Brooke Carter, Megan Andrews Dickerson, and Miranda Lane Oakley and eight great-grandchildren, all whom he loved dearly.
The family would like to express their gratitude for all of the visits, love and support through the years from friends and family.
In lieu of flowers, memorial contributions may be made to Mt. Zion Baptist Church, Building Fund, P.O. Box 280, Alamance, NC 27201.
Loflin Funeral Home, Liberty is honored to serve the Fogleman family.


This week we're looking at wellbeing. Wellbeing is not just a trendy buzzword but a matter of public health, and a concern of governments, employers and businesses.
The recent BBC Loneliness Experiment has busted some common myths about loneliness and found that, for example, young people suffer from loneliness more than older people.
Provided that you're happy with your life and are interested in living forever, this New Yorker article gives a comprehensive overview of what science, technology and committed immortalists can do for you.
The sociologist Nikolas Rose will be talking about his new book "Our Psychiatric Future" and addressing the question: "Is there an 'epidemic' of mental ill health in contemporary societies?" We've booked our spot!
In this brilliant 1955 ethnographic film, Jean Rouch portrays a peculiar ritual of the Hauka movement in Accra, Ghana, which is "a particular solution to the problem of the readjustment" and a representation of the West.


in Ontario. He obtained his bachelor's degree from the University of Toronto in 1968 and his master's degree in 1969. Three years later, in 1972, he received his doctoral degree from the University of Cambridge in England, and then returned to the University of Toronto to take a post as professor.
Martin's doctoral research focused on the study of interstellar matter – gas molecules and grains of dust found between stars – which has remained his specialty throughout his career. He strives to understand its origin and its relationship to stars. To do this, he analyzes and interprets data gathered by telescopes on Earth and in space, and integrates these data into theoretical computer simulations.
During the first 15 years of his career, Martin concentrated mainly on interstellar dust. Subjected to the magnetic fields produced by various celestial objects, interstellar dust tends to align itself in space like a magnet. Any light passing through these particles likewise become aligned (the light becomes "polarized"), and by studying this light, it becomes possible to extract information about the composition and shape of the interstellar grains.
In 1984, Martin became the first person from the astronomy department at the University of Toronto to become a member of the Canadian Institute for Theoretical Astrophysics.
Not long after, in 1987, he became interested in the collisions affecting hydrogen molecules of interstellar gas clouds where stars are born. His contributions in this field have repercussions for our understanding of how the first stars of the Universe came to be.
Martin used the Orion nebula as a case study in 1991 to examine the conditions within nebulas and other regions where interstellar matter is concentrated. The results of these studies have improved our understanding of the evolution of galaxies in the Universe.
In 1995, he became a permanent member of the science committee that manages the Canadian Galactic Plane Survey, a project that aims to map a large portion (60 degrees) of our galaxial plane at several different wavelengths using a common resolution. Martin uses the collected data to study the formation of massive stars and the evolution of interstellar dust, as well as its influence on the ecology of our galaxy.
In 1999, Martin was named Director of the University of Toronto's David Dunlap Observatory. He is currently involved in a number of international research projects, including the James Webb Space Telescope (the successor of the Hubble Space Telescope), the Herschel Space Observatory, the Spitzer Space Telescope, and the Planck space mission.
Martin received the Carlyle S. Beals Award in 1994 from the Astronomical Society of Canada in recognition of his research contributions.
Peter Gordon Martin talks about interstellar dust.
When I was a graduate student, interstellar dust was thought to be largely icy; what one now thinks of as cometary material. But in fact, we found out in the meantime that dust is not that at all. It is more like rocks, silicates and small pieces of carbon, which you might characterize as soot. The other things that we discovered are that there is a wide range of particles sizes: they are not all one tenth of a micron in size. Some are as small as molecules; in fact, there is a borderline area in which you can think of them as very tiny dust grains or as very large molecules. Some of those large molecules are like small sheets of graphite with hydrogen attached to them: we call these polycyclic aromatic hydrocarbons and they can be found in terrestrial counterparts like mothballs, for example. Automobile soot is another good representation of a mix of these compounds.


This free sample job ad for an executive marketing manager has an accompanying executive management marketing sample resume and executive management marketing sample cover letter to help you put together a winning job application.
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Home mortgage rates are stable and just above the recent record lows set this past month. Fixed conforming 30 year mortgage rates are averaging 4.32 percent, above the record low of 4.28 percent set on September 24, 2010. Fixed 30 year Vermont mortgage rates are averaging 4.36 percent.
15 year fixed conforming home mortgage rates are averaging 3.82 percent, up from an average 15 year home mortgage rate of 3.81 percent this past Friday. 15 year mortgage rates today in Wisconsin are also averaging 3.82 percent.
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text:
How Bristol's universities fared in The Times and The Sunday Times Good University Guide 2018
Bristol University has dropped three places after a 'challenging year'
It is viewed as one of the most prestigious and accurate of all the educational league tables.
And this year, both universities in Bristol have moved places within the Times and The Sunday Times Good University Gudie.
And it's bad news for both institutions, West of England fell by one place and Bristol University dropped three rankings.
UWE has slipped one place to 58, while the University of Bristol is now 19, down from 16 last year.
According to judges, the university ranked outside of the top 100 in the UK for teaching quality and their wider student experience, contributing to the slip in Bristol's overall performance.
Professor Judith Squires, Pro Vice-Chancellor for Education, at Bristol University said the results were 'disappointing given the quality' of the institution's provision but perhaps reflected a 'challenging year'.
(Image: (C) James Beck 2016)
She said: "It is good to note that we have nonetheless maintained our position within the top 20 Universities nationally. This is a positive reflection of our excellent graduate prospects and academic reputation.
Calls for Bristol's libraries to be linked with museums
"Providing our students with the best possible experience during their time at Bristol is a key priority for us and we work in close partnership with our students on a wide range of educational enhancement initiatives to achieve this aim.
"Current initiatives include the creation of a new campus heart, with more study spaces and a refurbished indoor sports centre, and the delivery of our flagship Bristol Futures curriculum. We are also introducing a new Wellbeing Service to support the health and wellbeing of our students and staff.
(Image: Northcliffe Media)
Plan for new 24/7 crisis service for kids with mental health issues
"Some of the positive changes we have implemented are better recognised in other rankings such as a recently published Times Higher Education (THE) league table that placed Bristol sixth best in Europe for teaching excellence.
"Our staff remain committed to providing the highest possible standards of teaching and to helping our students achieve excellent outcomes."
'I feel like a trillionaire' - cleaner goes on holiday paid for by students
Bristol is attempting to diversify its intake by – among other initiatives – reducing its standard offer for English and history by a grade and trimming the GCSE demands for medicine.
Professor Steven West, Vice-Chancellor of UWE Bristol said the ranking was 'disappointing'.
He said: "It is has still been an incredible year for UWE Bristol.
"We are particularly proud to have climbed into the top 10 universities in the UK for student satisfaction this year, with a record 89% of UWE Bristol final year students satisfied with their course overall, and to be awarded the highest possible rating in the Teaching Excellence and Student Outcomes Framework (TEF) 2018, achieving GOLD Standard. Staff and students have strived over the past year to work towards making UWE Bristol an even greater place to study, that puts the students at the heart of what we do.
"We want to be a top 50 University by 2020 and our ambitious strategy to become the best university for practice-based learning is taking shape. I would like to say a huge thank you to our incredibly hard working staff and fantastic students who continue to help us to move forward towards this goal."

redpajama_set_name: RedPajamaCommonCrawl


text:
Sebanyak 12 item atau buku ditemukan
An advanced exposition of Islamic economics and finance
This book is a challenging inquiry using the foundational epistemology that establishes the Islamic worldview and its applications to all socio-scientific theory, issues and problems. This foundational premise of all socio-scientific inquiry is the epistemology of unity of knowledge in the Qur'an combined with the Guidance of the Prophet Muhammad (Sunnah) and extensive discourse in scientific reasoning. Such a foundational epistemology called Tawhid, the Oneness of Allah in the Qur'an, is challenging and quite a different worldview of socio-scientific inquiry than the existing thinking and practice that came to be known as "islamic Economics and Finance". The latter literature is embedded in mainstream economic theory and practice. It contributes to the understanding of the methodology emanating from the fundamental epistemology of Tawhid in socio-scientific reasoning.
ISBN 10 : IND:30000093958456
Judul : An advanced exposition of Islamic economics and finance
Pengarang : Masudul Alam Choudhury, Mohammad Ziaul Hoque, Mohammad Ziaul Hoque, Mohammad Ziaul Hoque,
Kategori : Business & Economics
Penerbit : Edwin Mellen Pr
Bahasa : en
Google Book : http://books.google.co.id/books?id=E_gJAQAAMAAJ&dq=inauthor:ziaul&hl=&source=gbs_api
This book is a challenging inquiry using the foundational epistemology that establishes the Islamic worldview and its applications to all socio-scientific theory, issues and problems.
Encyclopaedia of Islamic Economics
Judul : Encyclopaedia of Islamic Economics
Pengarang : Abdelhamid Brahimi, Khurshid Ahmad, Muhammad Nejatullah Siddiqi, Muḥammad Mundhir Qaḥf,
Kategori : Economics
Penerbit : Young Writers
Google Book : http://books.google.co.id/books?id=GEMySgAACAAJ&dq=inauthor:khurshid+ahmad&hl=&source=gbs_api
First International Conference on Islamic Economics
selected papers
ISBN 10 : UOM:39015022624350
Judul : First International Conference on Islamic Economics
Sub Judul : selected papers
Pengarang : Khurshid Ahmad,
Kategori : Religion
Google Book : http://books.google.co.id/books?id=u2g6AAAAMAAJ&dq=inauthor:khurshid+ahmad&hl=&source=gbs_api
Application of Islamic Banking Instrument (Murābaha) for Sugarcane Industry in Developing Countries \\ Journal of Islamic Economics, Banking and Finance .- 2013, Vol. 9, No. 1
ISBN 10 : OCLC:927441527
Judul : Application of Islamic Banking Instrument (Murābaha) for Sugarcane Industry in Developing Countries \\ Journal of Islamic Economics, Banking and Finance .- 2013, Vol. 9, No. 1
Pengarang : Lutfullah Saqib,
Halaman : 18
Google Book : http://books.google.co.id/books?id=8tNdAQAACAAJ&dq=intitle:islamic+banking&hl=&source=gbs_api
Training and Development of Human Capital in Islamic Banking Industry \\ Journal of Islamic Economics, Banking and Finance .- 2012, Vol. 8, No. 1
Judul : Training and Development of Human Capital in Islamic Banking Industry \\ Journal of Islamic Economics, Banking and Finance .- 2012, Vol. 8, No. 1
Pengarang : Norhanim Dewa,
Google Book : http://books.google.co.id/books?id=6d1xAQAACAAJ&dq=intitle:islamic+banking&hl=&source=gbs_api
Islamic economics
ekonomi syariah bukan opsi, tetapi solusi!
Judul : Islamic economics
Sub Judul : ekonomi syariah bukan opsi, tetapi solusi!
Pengarang : Veithzal Rivai, Andi Buchari, Andi Buchari, Andi Buchari,
Klasifikasi : 2x6.3
Call Number : 2x6.3 VEI i
Google Book : http://books.google.co.id/books?id=vKDsSAAACAAJ&dq=isbn:9790106394&hl=&source=gbs_api
Morality and Justice in Islamic Economics and Finance
øMankind is faced with a number of serious problems that demand an effective solution. The prevalence of injustice and the frequency of financial crises are two of the most serious of these problems. Consisting of an in-depth introduction along with a
Judul : Morality and Justice in Islamic Economics and Finance
Pengarang : Muhammad Umer Chapra,
Penerbit : Edward Elgar Publishing
Google Book : https://play.google.com/store/books/details?id=NPyYBAAAQBAJ&source=gbs_api
Ibn Khaldun's theory of development: does it help explain the low performance of
the present-day Muslim world?* 4.1 INTRODUCTION The development and
decline of economies and societies has been of interest to scholars throughout ...
Introduction to Islamic Economics
Theory and Application
Gain deeper insight into the principles and theory of Islamic economics Introduction to Islamic Economics: Theory and Application provides an overview of the organizing principles and fundamentals of an Islamic economy. With deep discussion of the characteristics, rationale, key institutions, objectives, and instruments at work, the book addresses the core economic principles underlying a system based on the foundational teachings of Islam, and examines the implications for economic policies. Social welfare, economic justice, market functionality, efficiency, and equity are explored from an Islamic perspective, and the role and instruments of fiscal and monetary policy in Islamic systems are used to illustrate contemporary applications. Universities around the globe are offering courses on Islamic economics and finance, but despite the industry's rapid growth, most research has been focused on the financial principles rather than underlying economic principles. The first book of its kind, Introduction to Islamic Economics brings all the key concepts together into one reference volume. By outlining the ways in which Islamic finance and Islamic economics interrelate, this book can help readers to: Develop an understanding of the Islamic economic system and its institutional scaffolding Differentiate between the major characteristics of the dominant conventional economy and one based on the fundamental sources of Islam Understand the conditions that must be met for a just, well-balanced, stable, and growing economy Clarify the role of State, public policy, and risk-sharing in the Islamic financial system The Islamic financial system is expanding quickly, and those looking to increase their relevance in a changing economic landscape must get up to speed. Introduction to Islamic Economics provides a comprehensive overview of underlying economic system offering a deeper understanding of the feature of the system. This book is an excellent complement to Introduction to Islamic Finance, 2E by Iqbal and Mirakhor.
Judul : Introduction to Islamic Economics
Sub Judul : Theory and Application
Pengarang : Hossein Askari, Zamir Iqbal, Abbas Mirakhor, Zamir Iqbal, Abbas Mirakhor,
Penerbit : John Wiley & Sons
Google Book : https://play.google.com/store/books/details?id=Xl64BQAAQBAJ&source=gbs_api
Thus today, the Western concept of development is focused on efficient
institutions; a political system that nurtures institutions and prohibits rent-seeking
activities and the confiscation of legitimate wealth; and a social outlook that
embraces ...
Essays in Islamic economics
"Precisely the kind of book that Western economists have been waiting for."-John Presley, former professor of economics, University of LoughboroughThis systematic and applied exposition places Islamic macroeconomics on a par with Keynsian and neo-classical theories. Ideal for undergraduates, it covers consumer behavior, the macro consumption function, banking and finance, production and investment, growth and development, and public policy.M. Fahim Khan is the president of the Islamic Society of Institutional Economics at the International Institute of Islamic Economics, Islamabad, Pakistan, and is the author of ten monographs on Islamic economics.
Judul : Essays in Islamic economics
Pengarang : M. Fahim Khan,
Penerbit : The Islamic Foundation
Google Book : http://books.google.co.id/books?id=kR_YAAAAMAAJ&dq=inauthor:fahim+khan&hl=&source=gbs_api
"Precisely the kind of book that Western economists have been waiting for.
Notes on Islamic Economics: Theories and Institutions
Notes on Islamic Economics: Theories and InstitutionsIn this book I present the basic theories and the institutional set up of Islamic economics. It consists of chapters one through eleven. Chapters one and two attempt to answer the questions of 'what is Islamic Economics' and 'why we need to study it', they also discuss issues of definition, scope, sources of knowledge and relevance. The third chapter deals with the basic values and ethical questions of Islamic economics both as a branch of science and as a study of the Islamic economic system. It briefly studies ethical values which affect economic behaviour of men and women like of efficiency, performance, self-reliance, perfection and benevolence. Chapter four discusses the fundamental institutional setup of the Islamic economic system. It argues that the Islamic institutional set up is based on nine pillars which are: 1) Property rights, 2) Exchange and contracts, 3) Economic freedom, 4) Market structure, 5) Wealth and income, 6) State of law, 7) Economic activity of the state, 8) Internal ethical monitor, and 9) Mercy and caring. The next two chapters are devoted to the discussion of the position and place of the state and the private sector in the system and the interrelations between them. The main argument of these chapters is that while the Islamic system adores private ownership and economic freedom, it assigns an important role to the state, in all its branches, and charges it with important functions including the management of public properties and natural resources, which may be huge under certain circumstances. Islamic theories of consumer behavior and producer behavior are discussed in chapters seven and eight. The basic theorem of these two chapters is that human beings are not one dimensional. They rather have a benevolent facet of their behaviors which is present both in the consumers and the firms as well. The analysis in these two chapters tries to take this behavioral element into consideration. Chapter nine revisits the market and deals with the structure of prices at the micro level. It argues that the market reforms introduced by the Prophet, pbuh, himself were very exemplary in setting the desired structure for the market on the basis of fair play of market forces of demand and supply without elements of injustice which may come from monopoly, financial power, unethical practices by some actors or insufficient power of enforcement of exchange contracts. At the same time the Islamic market structure does not assume conditions of perfect competition. The last two chapters are devoted to discussing the effect of market structure on the factors of production and on the allocation process of resources. They also discuss regulations in the market and their extent and effects.In this book as in the other three books of this series, I deal with the fundamental issues of Islamic economics and finance in a realistic yet innovative manner while observing fundamental relations, ties and inspiration from the basic sources of the Islamic religion and its Shari'ah rulings; the Qur'an and the Sunnah.
Judul : Notes on Islamic Economics: Theories and Institutions
Pengarang : Monzer Kahf,
Kategori : Banks and banking
Google Book : http://books.google.co.id/books?id=2MXpsgEACAAJ&dq=inauthor:monzer&hl=&source=gbs_api
Notes on Islamic Economics: Theories and InstitutionsIn this book I present the basic theories and the institutional set up of Islamic economics.


Dr. Doty is a gynecologist practicing in Portland, OR. She was educated at Rutgers, Robert Wood Johnson Medical School in 2010.
Dr. Mallek is a gynecologist practicing in Hillsboro, OR. He was educated at Oregon Health & Science University School of Medicine in 2006.
Dr. Shaw is a gynecologist practicing in Portland, OR. She was educated at Ohio University Heritage College of Osteopathic Medicine in 2000.
Dr. Dukhovny is a gynecologist practicing in Portland, OR. She was educated at Boston University School of Medicine in 2006.
Dr. Bernstein is a gynecologist practicing in Silverton, OR. He was educated at Universidad Nacional Autonoma de Honduras Facultad de Ciencias Medicas in 1979.
Dr. Tran is a gynecologist practicing in Portland, OR. She was educated at Keck School of Medicine of the University of Southern California in 2001.
Dr. Chambers is a gynecologist practicing in Hood River, OR. He was educated at University of Texas Medical School at Houston in 1989.
Dr. Gerken is a gynecologist practicing in Tillamook, OR. She was educated at American University of the Caribbean School of Medicine in 2002.
Dr. Amato is a gynecologist practicing in Portland, OR. She was educated at University of Toronto Faculty of Medicine in 1989.
Dr. Adler is a gynecologist practicing in Seaside, OR. He was educated at Indiana University School of Medicine in 1976.
Dr. Wu is a gynecologist practicing in Portland, OR. She was educated at Wayne State University School of Medicine in 2004.
Dr. Shaffer is a gynecologist practicing in Portland, OR. He was educated at Drexel University College of Medicine in 2001.
Dr. Wyatt is a gynecologist practicing in Portland, OR. She was educated at University of Wisconsin School of Medicine and Public Health in 1997.
Dr. Yildirim is a hematologist oncologist practicing in Longview, WA. He was educated at Hacettepe University Faculty of Medicine in 1989.
Dr. Caldwell is a gynecologist practicing in Portland, OR. She was educated at Touro University - California Touro University College of Osteopathic Medicine in 2005.


ECDN servers require connectivity to NTP server(s) to synchronize their clocks. When the ECDN server clocks go out of sync by more than 5 minutes, ECDN operations team will no longer have the ability to remotely login and help troubleshoot the issue, even if the "Remote assistance" checkbox setting has been enabled.
If you use public NTP servers, then you also need to update the corporate firewall configuration to allow outbound UDP traffic on port 123, to these destinations. Here's how you can confirm these settings.
Note the "refid" column value will be ".POOL.".
Note, the "refid" column value ".INIT." which indicates lack of connectivity to the public NTP server.
Please follow up with your local network administrator to update the firewall settings, or get information about what NTP servers may be available within your organization.
Do NOT update the NTP server settings directly in the ECDN server VM. Instead, go to the ECDN Management portal, and navigate to that specific "ECDN server details" page, and then click to update the network settings. When you save the settings, the new values will be auto-propagated to the ECDN server. This way your settings will be preserved across ECDN server upgrades.


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text:
DoCoMo Finds Handsets Cloned in China
Cell phones "cloned" by using IC cards from NTT DoCoMo Inc.'s FOMA handsets were found to have been used in China and other countries illegally, according to sources. In six cases, perpetrators are thought to have taken the IC cards from disused third-generation cell phones and inserted them into other handsets, creating cloned cell phones. DoCoMo, which had previously insisted cloning its cell phones was "technically impossible," admitted it had confirmed such clones had been found through in-house investigations.
UPDATE: DoCoMo has issued a coment [in Japanese] about this article stating that it's a legacy issue with older FOMA model handsets. They state the function to refuse a connection with the DoCoMo side switchboard was added as of February 2006.
Fifteen-digit identification numbers are programmed into FOMA's IC cards, and all of the information on the subscriber is encrypted. As the transmitters accept calls only when all the information about the subscriber coincides, a cell phone with an IC card taken from another phone will be unusable. The company found suspicious phone records, one of which indicated the subscriber made two calls almost simultaneously in Japan and China. It found such illegal use of cell phones in China, the Philippines and Ghana from August 2005 to February 2006. Continue >>
The company said it suspects someone illicitly obtained the FOMA handsets' IC cards, which contain data used to identify the handsets and the legitimate users, after the contracts were canceled. The data include the 15-digit ID numbers for the handsets, the phone numbers of each customer and another handset verification code.
The IC cards, which were apparently taken to the countries from Japan, appear to have been inserted into other mobile handsets in those countries, they said. In Japan, mobile handsets can't be used unless the telephone numbers, 15-digit handset numbers and handset verification codes match relevant data in each company's computer systems. But telephone exchange equipment in China and other countries sometimes allows handsets to be used only by confirming the handset ID numbers. Via: The Japan Times >>
DoCoMo to Buy China-Made Handsets
DoCoMo Pins 3G Hopes on New Handsets
DoCoMo to Release New 2G Handsets
DoCoMo finds software glitch on SH505i
DoCoMo 901i – Next Generation FOMA Handsets Debut
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The reaction dynamics of collisions involving halo or weakly bound nuclei, at energies around the Coulomb barrier, can be strongly affected by the structure of such nuclei. Very strong entrance channel effects have been observed on various reaction pocess such as, elastic scattering, fusion and direct reactions when comparing collision induced by the 6He and 11Be halo nuclei with the ones induced by their cores 4He and 10Be. Collisions induced by the stable weakly bound nuclei 6Li, 7Li show also some peculiarities in comparison to the ones induced by well bound nuclei; coupling with the break-up channel is in fact very important in reproducing low energy data. In this contribution an overview of our present understanding of the discussed topic will be given along with the discussion of some new preliminary results.