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\section{INTRODUCTION}

\begin{comment}
Recent advances in Millimeter Wave (mmWave) radar sensing technology have made it a great tool in autonomous
vehicles \cite{HawkEye} and search/rescue in high risk areas \cite{mobisys20smoke}.
For example,
in the areas of fire with smoke and toxic gas and hence low visibility,
it is impossible to utilize optical sensors such as LiDAR and camera
to find a safe route, and any time delay can potentially cost lives in those rescue scenes.
To construct maps in those heavy smoke and dense fog environments,
mmWave radar has been shown as an effective sensing tool \cite{mobisys20smoke,HawkEye}.
However it is challenging to use mmWave radar signals for object imaging and reconstruction, as
their signals are usually of low resolution, sparse, and highly noisy due to multi-path and
specularity. A few recent work \cite{HawkEye, superrf,mobisys20smoke} have made some progress
in generating 2D images based on mmWave radar. In this paper, we move
one step further to tackle a more challenging problem: reconstructing 3D object shapes based on raw sparse and
low-resolution mmWave radar signals.
\end{comment}

Recently the advantage of Millimeter Wave (mmWave) radar in object sensing in low visibility environment has
been actively studied and applied in autonomous
vehicles \cite{HawkEye} and search/rescue in high risk areas \cite{mobisys20smoke}.
However, further application of mmWave radar in object imaging and reconstruction is quite difficult because of
the characteristics of mmWave radar signals such as low resolution, sparsity, and large noise due to multi-path and
specularity. Recent work \cite{HawkEye, superrf,mobisys20smoke} attempts to design deep learning systems
to generate 2D depth images based on mmWave radar signals. 3DRIMR \cite{sun20213drimr} further introduces
an architecture that generates 3D object shapes based on mmWave radar, but there is still room for significant improvement
in order to produce  more satisfactory end results.

In this paper, we introduce Radar to Point Clouds (R2P), a deep learning model
that generates 3D objects in the format of dense and smooth point clouds that accurately resemble the 3D shapes of original objects
with fine details.
The inputs to R2P are simply some rough and sparse point clouds with possibly many inaccurate points
due to the imperfect conversion from raw mmWave radar data.
For example, they can be
directly converted from those 2D depth images generated from radar data, using systems such as \cite{HawkEye}
or the Stage 1 of 3DRIMR \cite{sun20213drimr}.
R2P can be used to replace the generator network in Stage 2 of 3DRIMR.
Recall that 3DRIMR's Stage 1
takes 3D radar intensity data as input and generates 2D depth images of an object,
which are then combined and converted to a rough and sparse point cloud to be used as
the input to Stage 2. The generator network of Stage 2
outputs the 3D shape of the object in the form of dense and smooth point cloud.
Even though 3DRIMR can give some promising results, its Stage 2's generator network design is still not quite
satisfactory. Specifically, the edges of generated point clouds are still blurry and their points
tend to be evenly distributed in space and thus do not give a clear sharp shape structure.
We introduce R2P in this paper to replace the Stage 2 of the original 3DRIMR, and we find that R2P
significantly outperforms 3DRIMR both quantitatively and visually.

\begin{comment}
, a deep neural network architecture (based on conditional GAN)
that takes as input raw mmWave radar sensing signals scanned from multiple
different viewpoints of an object,
and it outputs the 3D shape of the object in the format of point cloud.
The architecture consists of two generator networks in two stages.
In Stage 1, generator network $\bf{G_{r2i}}$ takes 3D radar intensity data as inputs and generates 2D depth images.
In Stage 2, generator network $\bf{G_{p2p}}$ takes as input a set of four 2D depth images
(results from Stage 1) of the object (from four different viewpoints)
and generates a dense smooth 3D point cloud of the object.
Generator $\bf{G_{r2i}}$ is jointly trained with
a discriminator network $\bf{D_{r2i}}$, which fuses 3D radar intensity data and 2D depth images (either
generated or ground truth images).
In addition, generator $\bf{G_{p2p}}$ is jointly trained with
a discriminator network $\bf{D_{p2p}}$.

3DRIMR architecture is designed to combine the advantages of Convolutional Neural Network (CNN)'s
convolutional operation
and the efficiency of point cloud representation of 3D objects.
Convolutional operation can capture detailed local neighborhood structure of a 3D object,
and point cloud format of 3D objects is more efficient and of higher resolution than 3D
shape representation via voxelization.
Specifically, $\bf{G_{r2i}}$ applies 3D convolution operation to 3D radar intensity data
to generate depth images.
Generator $\bf{G_{p2p}}$ represents a 3D object in point cloud format (unordered point set hence convolutional operation not applicable), so that it can
process data highly efficiently (in terms of computation and memory) and can express
fine details of 3D geometry.

In addition, because commodity mmWave radar sensors (e.g., TI's IWR6843ISK \cite{iwr6843})
usually have good resolution along range direction even without time consuming Synthetic Aperture Radar (SAR) operation, a 2D depth image generated by $\bf{G_{r2i}}$
can give us high resolution depth information from the viewpoint
of a radar sensor. Therefore, we believe that combining multiple such 2D depth images from multiple viewpoints
can give us high resolution 3D shape information of an object.
Our architecture design takes advantage of this observation by using
multiple 2D depth images of an object from multiple viewpoints
to form a raw point cloud and uses that as input to generator $\bf{G_{p2p}}$.
\end{comment}

	Our major contributions are summarized as follows.

		We propose Radar to Point Cloud (R2P),  a deep neural network model,

		to generate smooth, dense, and highly accurate point cloud representation of
		a 3D object with fine geometry details,
		based on rough and sparse point clouds with incorrect points. These input point clouds are directly
		converted from the 2D depth images of an object that are generated from raw mmWave radar sensor data, and thus characterized by
		mutual inconsistency and orientation/shape errors, due to the imperfect process to generate them.

			R2P utilizes an architecture of two sequential deep learning encoder-decoder blocks
			to extract the essential features of those input point clouds of an object when observed from
			various viewpoints, and to ensure the internal consistency of a generated output point cloud and
			its detailed shape reconstruction of the original object.
			We further demonstrate with extensive experiments the importance of loss function design
			in the training of models for reconstructing point clouds. We also show
			the limitations of Chamfer Distance (CD) and Earth Mover's Distance (EMD),
			 the two state-of-the-art point clouds evaluation metrics, in the evaluation of the shape similarity of two point clouds.

		\begin{comment}
					We develop an improved 3DRIMR system implementation that can conduct 3D object reconstruction of the signal fast collected

		by using a commodity
		mmWave radar sensor, instead of a slow full-scale SAR scan.
		The whole system takes advantage of convolutional operation and point cloud based neural network for efficient 3D shape generation with detailed geometry.

		\end{comment}

In the rest of the paper, we discuss related work and preliminaries in Sections
	\ref{sec_related} and \ref{sec_background}, and then the design of R2P in Section \ref{sec_design}.
	Experiment results are given in Section \ref{sec_imp}.
	Finally the paper concludes in Section \ref{sec_conclusion}.

\section{RELATED WORK}\label{sec_related}
Frequency Modulated Continuous Wave (FMCW) Millimeter Wave (mmWave) radar sensing
has been an active research area in recent years, especially in applications such as person/gesture identification \cite{vandersmissen2018indoor,yang2020mu}, car detection/imaging \cite{HawkEye}, and environment sensing \cite{mobisys20smoke, superrf}. Usually Synthetic Aperture Radar (SAR) is used in data collection
for high resolution, e.g., \cite{mamandipoor201460,national2018airport,ghasr2016wideband,sheen2007near}.
This paper is built on our recent work \cite{sun20213drimr}
on applying mmWave radar for 3D object reconstruction, in which we proposed 3DRIMR.
The deep neural network model proposed in this paper can replace the model in the Stage 2 of 3DRIMR,
and this new model significantly outperforms the original 3DRIMR.
There have been a few recent work on mmWave radar based imaging, mapping, and 3D object
reconstruction \cite{HawkEye,superrf,mobisys20smoke,yuan2018pcn,qi2016pointnet}. Our work is inspired by
their promising research results, especially PointNet \cite{qi2016pointnet,qi2017pointnet++}
and PCN \cite{yuan2018pcn}. Due to the low cost and small form factor of commodity mmWave
radar sensors, we plan to develop a simple 3D reconstruction system with fast data collection to
be attached in our UAV SLAM system \cite{sun2020lidaus} for search and rescue in dangerous environment.
Besides radar signals, vision community has also been working on learning-based
3D object shape reconstruction \cite{yang20173d,dai2017shape,sharma2016vconv,smith2017improved},
most of which use voxels to represent 3D objects.
Our proposed neural network model
uses point cloud as a format for 3D objects to capture detailed geometric information
with efficient memory and computation performance.
Our proposed R2P significantly outperforms the existing methods such as PointNet \cite{qi2016pointnet},
PointNet++ \cite{qi2017pointnet++}, PCN\cite{yuan2018pcn}, and 3DRIMR \cite{sun20213drimr}.

\begin{comment}
HawkEye \cite{HawkEye} generates 2D depth images of an object based on
conditional GAN architecture with 3D radar intensity maps obtained by multiple SAR scans along both elevation and azimuth dimensions. Our architecture adopts this design to generate intermediate results used as inputs to a 3D point cloud generator.
Different from \cite{HawkEye}, we use data of only two snapshots
along elevation dimension
when using commodity TI IWR6843ISK sensor \cite{iwr6843}.
This is similar to the input data used in \cite{superrf}, but the network of \cite{superrf}
outputs just a higher dimensional radar intensity map along elevation dimension, not 2D depth images.
\end{comment}

\section{PRELIMINARIES}\label{sec_background}

\subsection{FMCW Millimeter Wave Radar Sensing and Imaging}

\begin{comment}
sensor works
by periodically transmitting continuous chirps that each linearly sweeps through
a certain frequency band \cite{timmwave}.
Transmitted signals reflected back from an object in a 3D space will be received
by a receiver. Range Fast Fourier Transform (FFT) is conducted on the received waveforms
to detect the distance of the object from the radar sensor.
In addition, multiple receivers can be arranged horizontally and vertically
to form virtual antenna arrays along both dimensions. Then
two additional FFTs can be applied to the data to calculate an object's relative angles from
the sensor horizontally and vertically, referred to as azimuth angle $\phi$
and elevation angle $\theta$.
Those three FFTs together can generate a 3D heatmap or intensity map of the space that
represents the energy or radar intensity per voxel, which is written as $x(\phi, \theta, \rho)$.

The process of electronically or mechanically steering an antenna array
to get high azimuth and elevation angle resolutions
is referred to as Synthetic Aperture Radar (SAR) operation.
Usually higher resolutions along these two dimensions requires longer SAR operation time given fixed number
of transmitters and receivers. However, high range resolution can be achieved even
with commodity mmWave radar sensors
(e.g., IWR6843ISK \cite{iwr6843}) without time consuming SAR process.
In our work, we use IWR6843ISK \cite{iwr6843} operating at $60$ GHz frequency.

Different from LiDAR and camera sensors, the data generated by mmWave radar sensors is usually
sparse, of low resolution, and highly noisy.
Even though SAR can help improve resolution, it is a very slow process,
which may not be practical in many application scenarios that require short application response time.
The specularity characteristic makes an object's surface behave like a mirror, so the reflected signals from
a certain portion of the object will not be received by a receiver (hence missing data).
In addition, the multi-path effect can cause ghost points which give incorrect information on
the object's shape.
For detailed discussions on FMCW mmWave radar sensing, please see \cite{HawkEye,superrf,mobisys20smoke}.

However such representations have cubic growth rate in the number of voxel grids, so they
are limited to representing low resolution objects.
In addition, mesh representations of 3D objects are considered in existing work, e.g.,
\cite{kong2017using,wang2018pixel2mesh}, but they are also limited by memory cost and are prone to self-intersecting meshes.

In addition, even though we can generate the point cloud of an object by directly filtering out
mmWave radar 3D heatmaps, such a resulting point cloud usually has very low resolution, being sparse,
and with incorrect ghost points due to multi-path effect.

Therefore even though it may be acceptable to
just use such a point cloud to detect
the presence of an object, it is impossible
to reconstruct the shape of the object. Our work attempts to solve
this problem by using two generator neural networks
to produce a smooth dense point cloud based on raw radar data.

Other than point clouds, voxel representations are commonly used in 3D reconstruction to represent 3D objects in learning based approaches,
for example, \cite{kar2017learning,paschalidou2018raynet,ji2017surfacenet,wu2016learning}, and
3D CNN convolutional operations can be applied to such data models.
However such representations have cubic growth rate in the number of voxel grids, so they
are limited to representing low resolution objects.
In addition, mesh representations of 3D objects are considered in existing work, e.g.,
\cite{kong2017using,wang2018pixel2mesh}, but they are also limited by memory cost and are prone to self-intersecting meshes.
\end{comment}

Similar to \cite{sun20213drimr}, we use FMCW mmWave radar
sensor \cite{timmwave} signals to reconstruct 3D object shapes.
Fast Fourier Transform (FFT) along three directions are conducted  on received waveforms
to generate 3D intensity maps of a space that
represent the energy or radar intensity per voxel in the space, written as $x(\phi, \theta, \rho)$.
Note that  $\phi$, $\theta$, and $\rho$ represent azimuth angle, elevation angle, and range respectively.
We use IWR6843ISK \cite{iwr6843} operating at $60$ GHz frequency,
and for high resolution radar signals, we adopt SAR operation.
Unlike data from LiDAR and camera sensor, mmWave radar sensors can only give us
sparse, low resolution, and highly noisy data. Incorrect ghost points in radar signals
can be generated due to multi-path effect. References \cite{HawkEye,superrf,mobisys20smoke} give more detailed discussion
on FMCW mmWave radar sensing.

\subsection{Representation of 3D Objects}
We adopt point cloud format to represent 3D objects.
Even though point cloud is a standard representation of 3D objects and it is used in
learning-based 3D reconstruction, e.g., \cite{fan2017point,qi2016pointnet,qi2017pointnet++}, the convolutional operation
of Convolutional Neural Network (CNN) cannot be directly applied to a point cloud set as it is
essentially an unordered point set.  Furthermore, the point cloud of an object that is directly generated by
raw radar signals is not a good choice to represent the shape of an object because of radar signal's
low resolution, sparsity, and incorrect ghost points due to multi-path effect.
Besides point clouds,  voxel-based representations can also be used in 3D reconstruction \cite{kar2017learning,paschalidou2018raynet,ji2017surfacenet,wu2016learning},
and the advantage of such representation is that 3D CNN convolutional operations can be applied.
In addition, mesh representations of 3D objects are also used in existing work \cite{kong2017using,wang2018pixel2mesh}.
However these two representation formats are limited by memory and computation cost.

\begin{comment}
In this work, we use point cloud, a widely used format in robotics, to represent 3D objects' geometry (e.g., \cite{mobisys20smoke}). This format has been used in recent work
on learning-based 3D reconstruction, e.g., \cite{fan2017point,qi2016pointnet,qi2017pointnet++}.
A point cloud representation of an object is a set of unordered points, with each point is a sample point of
the object's surface, written as its 3D Cartesian coordinates.
Unlike voxelization of 3D objects, a point cloud can represent
an object with high resolution but without high memory cost.
However, CNN convolutional operation cannot be applied to an unordered point cloud set.
\end{comment}

\subsection{Review of 3DRIMR Architecture}\label{review_3drimr}
This paper introduces R2P as a generator network to replace the Stage 2 of 3DRIMR
in order to generate smooth and dense point clouds.
For completeness, we now briefly review 3DRIMR's architecture.
3DRIMR consists of two back-to-back generator networks
$\mathbf{G_{r2i}}$ and $\mathbf{G_{p2p}}$.
In Stage 1, $\mathbf{G_{r2i}}$ receives a 3D radar energy intensity map
of an object and outputs a 2D depth image of the object.
We let a mmWave radar sensor scans an object from multiple viewpoints
to get multiple 3D energy maps. Then  $\mathbf{G_{r2i}}$ generates
the corresponding 2D depth
images of the object.
The Stage 2 of 3DRIMR first pre-processes these images to get multiple coarse point clouds of the object,
which are used as the input to $\mathbf{G_{p2p}}$ to generate a single point cloud of
the object.
A conditional generative adversarial network (GAN) architecture is designed for 3DRIMR's training. That is,
two discriminator networks $\mathbf{D_{r2i}}$ and $\mathbf{D_{p2p}}$ that are jointly trained together with their corresponding generator networks.
Let $m_r$ denotes a 3D radar intensity map of an object captured
from a viewpoint, and
let $g_{2d}$ be a ground truth 2D depth image of the same object captured
from the same viewpoint.
$\mathbf{G_{r2i}}$ generates $\hat{g}_{2d}$ that predicts or estimates $g_{2d}$ given $m_{r}$.
If there are $k$ different viewpoints $v_1, ..., v_k$,
generator $\mathbf{G_{r2i}}$ predicts their corresponding 2D depth images $\{\hat{g}_{2d,i} | i = 1, ..., k\}$.
Each $\hat{g}_{2d,i}$ can be directly converted to a coarse and sparse 3D point cloud.
Then we can have $k$ coarse point clouds $\{P_{r,i} | i = 1, ..., k\}$
of the object.
The Stage 2 of 3DRIMR unions the $k$ coarse point clouds
to form an initial estimated coarse point cloud of the object, denoted as $P_r$, which is
a set of 3D points $\{p_j | j = 1, ..., n\}$.
Generator $\mathbf{G_{p2p}}$ takes
$P_r$ as input, and outputs a dense and smooth point cloud $P_o$.
Note that since the generation process of $\mathbf{G_{r2i}}$ is not perfect,
a coarse $P_r$ may likely contain many missing or even incorrect points.
Next we will discuss our proposed R2P.

\section{R2P Design}\label{sec_design}

\subsection{Overview}

R2P is a generative model that generates a smooth and dense 3D point cloud of an object
from the union of multiple coarse point clouds, which are directly
converted from the 2D depth images of the object observed from different viewpoints.
Since those 2D depth images are generated from raw radar energy maps, the union of
their converted point clouds may contain many inconsistent and incorrect points due to the imperfect image generation process.
R2P network model is able to extract the essential features of those input point clouds,
and to ensure the internal consistency of a generated output point cloud
and its detailed, accurate shape reconstruction of the original object.
R2P can be used to replace the generator network of the Stage 2 of 3DRIMR,
and it can also be used as an independent network that works on any rough and sparse input point clouds that contain incorrect points.
The architecture of R2P is shown in Fig. \ref{fig_G}.
Let $P_r$ denote a union of $k$ separate coarse point clouds observed from $k$ viewpoints of the object $\{P_{r,i} | i=1,...,k\}$.
R2P aims at generating a point cloud of an object
with continuous and smooth contour $P_o$ from $P_r$.
In our design we also include a discriminator network to train the generator under GAN framework. Due to space limitations,
we do not discuss the discriminator here.

\begin{figure}
	\includegraphics[width=\textwidth]{figures/R2P.eps}
	\caption{R2P network architecture.
		The system first pre-processes multiple 2D depth images of an object
		to get multiple coarse and sparse point clouds, which may contain many incorrect points.
		These depth images are generated for the same object but viewed from four different viewpoints.
		Combining those coarse point clouds we can derive a single coarse point cloud,
		which is used as the input of R2P. Then
		R2P outputs a dense, smooth, and accurate point cloud representation of the object.} \label{fig_G}
\end{figure}

\subsection{R2P Architecture}

R2P's input $P_r$ and output $P_o$
are 3D point clouds represented as $n \times 3$ matrices, where $n$ is the number of points in the point cloud,
and each row represents the 3D Cartesian coordinate $(x, y, z)$ of a point.
Note that the output point cloud generated from our network has a larger number of points than the input point cloud,
i.e., our network can reconstruct a dense and smooth point cloud from a sparse one.
R2P consists of two sequential processing blocks, and both blocks share the same encoder-decoder network design.
The first block takes the raw input point cloud $P_r$ to produce
an intermediate point cloud $P_m$, and then the second block processes $P_m$ to generate the final output $P_o$.
In each block,
the encoder takes its input point cloud and converts it to a high dimensional feature vector,
and the decoder takes this feature vector and converts it to an intermediate or a final output point cloud.

\noindent \textbf{Encoder.}
As shown in Fig. \ref{fig_G}, in the first block, the encoder gets $P_r$  as input,
and passes it to a shared multilayer perceptron (MLP) to extend every point $p_i$ in $P_r$ to a high dimensional feature vector $f_i$
and form the point feature matrix $F^1$.
This shared MLP is a network with two linear layers with BatchNorm and ReLU in between.
Then, the encoder applies a point-wise maxpooling on $F^1$ and extracts a global feature vector $g^1$.
To produce a complete point cloud for an object,
we need both local and global features,
therefore, the encoder concatenates the global feature with each of the point features
$f^1_i$ (of $F^1$) and form the complex feature matrix $F^1_c$. Then another shared MLP is used to produce
point feature matrix $F^1_p$.
Then, another point-wise maxpooling will perform on $F^1_p$ to extract the final global feature $g^1_f$.

\begin{comment}
$F^1_P$ (or $F^2_P$).
Then, another point-wise maxpooling will perform on it and extract the final global feature $g^1_f$ (or $g^2_f$).
or $P_m$ in step 2 as input,
and passes it to a shared MLP to extend every point $p_i$ in $P_r$ (or $P_m$) to high dimensional feature vector $f_i$
and form the point feature matrix $F^1$ (or $F^2$).
This shared MLP is a simple network with two linear layers with BatchNorm and ReLU in between.
Then, we apply a point-wise maxpooling on it and extracts a global feature vector $g^1$ (or $g^2$).
To produce a complete point cloud for an object,
we need both local and global features,
therefore, we concatenates the global feature with each of the point features
$f^1_i$ and form the complex feature matrix $F^1_P$ (or $F^2_P$).
Then, another point-wise maxpooling will perform on it and extract the final global feature $g^1_f$ (or $g^2_f$).
\end{comment}

\noindent \textbf{Decoder.}
Decoder takes the final global feature $g^1_f$ as input,
and then passes it to a MLP, which consists of three fully-connected layers with ReLU in between.
After this MLP, the final global feature is converted to $1 \times 3m$ vector (where $m$ is the number of points in $P_m$),
and then reshaped to $m \times 3$ matrix which represents the point cloud $P_m$.

As shown in Fig. \ref{fig_G}, the second block's design is similar to the first block.  The encoder of the second block
takes $P_m$ as input to produce a final global feature $g^2_f$, and then the decoder generates
the final output point cloud $P_o$.

\begin{comment}
\bigskip
\noindent \textbf{Remarks.}
Note that there are two major differences between the proposed generator network and
the generator in Stage 2 of 3DMIMR \cite{sun20213drimr}.
First, the proposed generator is ``deeper" than the generator of 3DMIMR
as ours has more layers of {DeepPoint} blocks.
Second, 3DMIMR uses fully connected layers and apply reshape operation to derive output point cloud from a high-dimensional global feature vector,
which can only get an rough overall shape without many fine, local, and detailed characteristics.
However, our new generator network design
generates an output point cloud from both local and global features
and hence can generate fine, local and detailed characteristics of an object.
\end{comment}

\begin{comment}
\subsection{Discriminator $\mathbf{D_{p2p}}$}

We design an improved discriminator with two-stream inputs, as shown in Fig. \ref{fig_stage_2}.
The inputs pass through a simple shared MLP and get expanded into higher dimensional matrices.
A point-wise max pooling and an average pooling are used to extract two global feature vectors.
Then the two global feature vectors are concatenated to form final global features,
which are further concatenated and fed into two fully connected layers to derive a score.
The score is used to indicate whether the input is real or fake, i.e., generated point cloud.
\end{comment}

\subsection{Loss Function}

Due to the irregularity of point clouds,
it is quite difficult to choose an effective loss function to indicate the difference between a generated point cloud and
its corresponding ground truth point cloud.
There are two popular metrics to evaluate the difference between two point clouds:
Chamfer Distance (CD) \cite{yuan2018pcn} and Earth Mover's Distance (EMD) \cite{yuan2018pcn}.
CD calculates the average closest distance between two point clouds $S_1$ and $S_2$.
The symmetric version of CD is defined as:
\begin{equation}
\small{
	CD(S_1, S_2) =
	\frac{1}{|S_1|} \sum_{x \in S_1} \mathop{min}\limits_{y \in S_2} \lVert x-y \rVert_{2}
	+ \frac{1}{|S_2|} \sum_{y \in S_2} \mathop{min}\limits_{x \in S_1} \lVert y-x \rVert_{2}
}
\end{equation}
EMD can find a bijection $\phi: S_1 \to S_2$,
which can minimize the average distance between each pair of corresponding points in two point clouds $S_1$ and $S_2$.
EMD is calculated as:
\begin{equation}
\small{
	EMD(S_1, S_2) =
	\mathop{min}\limits_{\phi: S_1 \to S_2} \frac{1}{|S_1|}
	\sum_{x \in S_1} || x-\phi(x) ||_{2}
}
\end{equation}
In our system, R2P generates the intermediate point cloud $P_m$ after its first encoder-decoder block,
and then generates the final output point cloud $P_o$ after the second block.
Hence, we design our loss function to evaluate both generated point clouds. That is,
R2P's loss function is defined as a weighted sum of the loss of the first block $d_1(P_m, P_{gt})$
and the loss of the second block $d_2(P_o, P_{gt})$.
\begin{equation}
	L(R2P) =  d_1(P_m, P_{gt}) + \alpha d_2(P_o, P_{gt})
\end{equation}
Both $d_1$ and $d_2$ can be either CD or EMD, or some weighted combination of them.
$\alpha$ is a hand-tuned parameter, and in our experiments, we find it performs well when set to 0.1.
Note that unlike CD, EMD needs to find a bijection relationship between two point clouds,
which is a optimization problem and hence computationally expensive, especially when the point clouds have large amounts of points.
Moreover, this bijection also requires these two evaluated point clouds have the same number of points.

\begin{comment}
When training the network, we concurrently train $\mathbf{D_{p2p}}$ to minimize $\mathcal{L}_{\mathbf{D_{p2p}}}$,
and train $\mathbf{G_{p2p}}$ to minimize $\mathcal{L}_{\mathbf{G_{p2p}}}$.
$\mathcal{L}_{\mathbf{D_{p2p}}}$ is calculated as the mean MSE (Mean Square Error) of $\mathbf{D_{p2p}}$'s prediction error.
The loss function of generator $\mathcal{L}_{\mathbf{G_{p2p}}}$ is a weighted sum,
consisting of $\mathcal{L}_{GAN} (\mathbf{G_{p2p}})$,
Chamfer loss $\mathcal{L}_{cf}$
between predicted point clouds and the ground truth,
and EMD \cite{yuan2018pcn} loss $\mathcal{L}_{emd} (\mathbf{G_{p2p}})$.

Note that Chamfer distance \cite{yuan2018pcn} calculates the average closest distance between input and output points.
The symmetric version of it is defined as:

Then, Chamfer loss isdefined as:
\begin{equation}
\mathcal{L}_{cf} (\mathbf{G_{p2p}}) = d_{cf}(\hat{P}_r, P_{true})
\end{equation}
In addition, Earth Mover's Distance (EMD) \cite{yuan2018pcn} can find a bijection $\phi: S_1 \to S_2$,
which can minimize the average distance between pairs of corresponding points.
The equation of EMD is:
\begin{equation}
\small{
	d_{emd}(S_1, S_2) =
	\mathop{min}\limits_{\phi: S_1 \to S_2} \frac{1}{|S_1|}
	\sum_{x \in S_1} || x-\phi(x) ||_{2}
}
\end{equation}
In our case, EMD loss is calculated as:
\begin{equation}
\mathcal{L}_{emd} (\mathbf{G_{p2p}}) = d_{emd}(\hat{P}_r, P_{true})
\end{equation}

$\mathcal{L}_{\mathbf{G_{p2p}}}$ is given by Eqn. (\ref{eqn_L_G_p2p}).
Note that $\lambda_{d_{cf}}$ and $\lambda_{d_{emd}}$ are hand-tuned to 100 and 1 respectively
in our experiments.
\begin{eqnarray}\label{eqn_L_G_p2p}
\mathcal{L}_{\mathbf{G_{p2p}}} &=& \mathcal{L}_{GAN} (\mathbf{G_{p2p}})
+ \lambda_{d_{cf}}\mathcal{L}_{cf}(\mathbf{G_{p2p}})  \nonumber \\
&&+ \lambda_{d_{emd}} \mathcal{L}_{emd} (\mathbf{G_{p2p}})
\end{eqnarray}

\end{comment}

\section{IMPLEMENTATION AND EXPERIMENTS}\label{sec_imp}

We implement our proposed R2P network and use it as the generator network of
3DRIMR system's Stage 2.
The system first generates 2D depth images from 3D radar intensity maps from multiple views of an object,
and then passes these output depth images to R2P to produce a 3D point cloud of the object.

\subsection{Datasets}
We conduct experiments on a dataset including 5 different categories of objects,
namely industrial robot arms, cars, chairs, desks, and L-shape boxes.
This dataset consists of both synthesized data and real data collected from experiments.
The input point clouds to R2P are the output depth images produced by 3DRIMR's Stage 1.
We follow a procedure that is similar to \cite{sun20213drimr} to generate ground truth point clouds.
In order to obtain enough amount of data required to train our deep neural network models,
we modified HawkEye's data synthesizer \cite{web:hawkeyeCode} to synthesize 3D radar intensity maps and 2D depth images from 3D CAD point-cloud models, with configurations matching TI's mmWave radar sensor IWR6843ISK with DCA1000EVM \cite{dca1000evm},
and Stereolabs' ZED mini camera \cite{zed}.
In our experiments and when generating synthesized data,
we determine the space setup to capture the views of various objects in a 3D environment.
Four pairs of mmWave radar sensors and depth camera sensors are placed at the centers of the four edges of a square area,
pointing towards the center of the square area where objects are randomly placed.

\begin{comment}
After an object's placement is determined, the occluded bodies will be removed for each view respectively through spherical projection to obtain the point cloud of visible surfaces. These points will be used as point reflectors for camera sensors, and further modeled with radar cross-sections as point reflectors for radar sensor \cite{HawkEye}.
We generate depth images from camera point reflectors \cite{web:pc2img} through perspective projections matching the pinhole camera model. To obtain a better depth resolution, we scale the depth range based on the scenario setup.
Clusters of background noise reflectors are added to the radar point reflectors of objects to perform ray tracing and received signal simulation. Thermal noise and error in antenna positions are also introduced.
After we generate 3D mmWave radar intensity map in spherical coordinate system, we convert it into Cartesian coordinate system by focusing on the center area for object placements, then mapping intensity values onto the Cartesian voxels from the closest spherical voxels, while maintaining the size of the intensity map.
\end{comment}

\subsection{Model Training and Testing}
We use the 2D depth images generated in the Stage 1 of 3DRIMR
to form a dataset of coarse and sparse input point clouds for the $5$ different categories of objects.
Each input point cloud has $1024$ points and an intermediate/final output point cloud has $4096$ points.
We train our proposed network model for each category independently.
For each object category, we train the model based on $1400$ pairs of point clouds for $200$ epochs with batch size $2$.
The learning rate for the first 100 epochs is $2 \times 10^{-4}$ and linearly decreases to 0 in the rest 100 epochs.
Then we test the model using the remaining $100$ point clouds.

\subsection{Evaluation Results}

To the best of our knowledge, except for our previous work 3DRIMR,
there are no other point cloud-based networks to
reconstruct smooth, dense, and accurate point clouds from \textit{point clouds with many incorrect and inconsistent points},
e.g., the union of multiple coarse point clouds which are converted from various 2D depth images of an object
that are possibly inaccurate and inconsistent between themselves in terms of orientation and shape structure details.
For example, a generated 2D depth image from radar data \cite{HawkEye,sun20213drimr} can possibly
have a car's left and right sides switched, or it can be shaped like a similar but different car with different shape details.
Note that the existing works (e.g., \cite{yuan2018pcn}) on this subject usually do not have such strong inaccuracy assumption on their inputs except missing points.
Nevertheless we compare our proposed architecture against five related baseline methods.
They are popular point cloud-based generative models, mainly designed for the purposes
of classification, segmentation, and point clouds completion, assuming input data is either sparse or there are missing points.
To ensure fair comparison, we train all six models (including baselines and ours)
based on the same training and testing datasets.
These baseline methods include:
	(1) \textbf{PointNet}.
	It is introduced in \cite{qi2016pointnet}. We choose $1024$ as the dimension of global feature of PointNet.
	The loss function is a combination of CD and EMD.
	(2) \textbf{PointNet++}.

	We use the same encoder architecture of PointNet++ \cite{qi2017pointnet++} for classification with three Set Abstraction (SA) modules to get a 1024-dimension global feature, followed by a decoder consists of 3 fully-connected layers.
	The loss function is also a combination of CD and EMD.
 	(3) \textbf{PCN\_CD}.
	We use the same architecture of PCN\cite{yuan2018pcn}, and set the grid size as 2.
	The number of points in the coarse and detailed output point clouds are 1024 and 4096 respectively.
	Note that in this method, both $d_1$ and $d_2$ are CD.
	(4) \textbf{PCN\_EMD}.
	In this method, the architecture of the network is same as PCN\_CD,
	but $d_1$ is EMD.
	(5) \textbf{3DRIMR}.
	This is the only architecture designed for point clouds reconstruction from inaccurate input point clouds among the $5$ baselines.
	To ensure fair comparison, we use the combination of CD and EMD as its loss function.
\begin{figure*}[htb!]
	\centerline{
		\begin{minipage}{1\linewidth}
			\begin{center}
				\setlength{\epsfxsize}{0.95\linewidth}
				\epsffile{figures/plots/cd.eps}
				\setlength{\epsfxsize}{0.95\linewidth}
				\epsffile{figures/plots/emd.eps}\\
				{}
			\end{center}
			\caption{Quantitative comparison on datasets of $5$ different objects using baseline methods and our method. Top: Chamfer Distance of the output point cloud and the ground truth.
				Bottom: Earth Mover's Distance of the output point cloud and the ground truth.} \label{bar_chart}
			\label{6methods_plot}
		\end{minipage}
	}
\end{figure*}
We compare the performance of the two variants of our proposed R2P,
labeled as R2P\_CD (both $d_1$ and $d_2$ use CD) and R2P\_EMD  (both $d_1$ and $d_2$ use EMD),
with the five baseline methods mentioned above in terms of CD and EMD.
The results are shown in Fig. \ref{6methods_plot}.
We can see that except the case of robot arms,
our methods always have the smallest CD or EMD loss among all the methods.
Even for the robot arms, the performance of our methods are similar to the other five methods.

We further compare the results of all six methods by visually examining their output points clouds, and some of them are shown in Fig. \ref{pc_plots}.
We can see that all the output point clouds are coarser than the ground truth point clouds
and some of them lose many detailed shape characteristics. However,
R2P\_EMD can give the best shape reconstruction among all the methods, keeping most geometry characteristics.
Especially for some small objects with fine shape details like chairs,
only our method R2P\_EMD can reconstruct an object with accurate shape.

\begin{table}[h]
	\small
	\centering
	\begin{tabular}{l c c c c c}

		Input
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/arm-in.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/car-in.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/chair-in.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/desk-in.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/box-in.eps}}
		\end{minipage}
		\\

		PointNet++
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/arm-02.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/car-02.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/chair-02.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/desk-02.eps}}
		\end{minipage}
		&
		\begin{minipage}[b]{0.16\columnwidth}
			\centering
			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/box-02.eps}}
		\end{minipage}
	 	\\

	 	PCN\_EMD
	 	&
	 	\begin{minipage}[b]{0.16\columnwidth}
	 		\centering
	 		\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/arm-04.eps}}
	 	\end{minipage}
	 	&
	 	\begin{minipage}[b]{0.16\columnwidth}
	 		\centering
	 		\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/car-04.eps}}
	 	\end{minipage}
	 	&
	 	\begin{minipage}[b]{0.16\columnwidth}
	 		\centering
	 		\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/chair-04.eps}}
	 	\end{minipage}
	 	&
	 	\begin{minipage}[b]{0.16\columnwidth}
	 		\centering
	 		\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/desk-04.eps}}
	 	\end{minipage}
	 	&
	 	\begin{minipage}[b]{0.16\columnwidth}
	 		\centering
	 		\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/box-04.eps}}
	 	\end{minipage}
 		\\

 		3DRIMR
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/arm-051.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/car-052.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/chair-052.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/desk-052.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/box-05.eps}}
 		\end{minipage}
 		\\

 		R2P\_EMD
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/arm-07.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/car-07.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/chair-072.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/desk-07.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/box-07.eps}}
 		\end{minipage}
 		\\

 		Ground Truth
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/arm-gt.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/car-gt.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/chair-gt.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/desk-gt.eps}}
 		\end{minipage}
 		&
 		\begin{minipage}[b]{0.16\columnwidth}
 			\centering
 			\raisebox{-.5\height}{\includegraphics[width=\linewidth]{figures/pc-figures/box-gt.eps}}
 		\end{minipage}

	\end{tabular}
	\captionof{figure}{Comparison of generated point clouds of different objects using different methods.
		The output point clouds of PointNet, PCN\_CD, R2P\_CD are very similar to those of
		PointNet++, PCN\_EMD, R2P\_EMD, respectively.  They are not shown here due to space limitations. }
\label{pc_plots}
\end{table}

\subsection{Performance of Different Loss Functions}

Loss function plays a quite important role in model training.
A well-designed loss function can not only speed up the training process,
but also affect the performance of a deep learning model.
On the other hand, a bad loss function may not converge even with a well-designed network model.
Hence, we need to carefully design our loss function used when training our model.
However, existing popular evaluation metrics to compare point clouds are CD and EMD,
which can only evaluate the overall distance between a pair of point clouds
but cannot effectively capture the similarity of their shapes.

To search for a good loss function for R2P,
we conduct a series of experiments with different combinations of these two metrics.
Except the loss functions used in training are different,
all other settings are all the same for these experiments.
The quantitative results are shown in Fig. \ref{loss_plot}.
We can see that if we only use EMD in the loss function, which is L2 in the figure,
the CD between output and ground truth point clouds will be large;
and except in the box experiment,
if we only use CD in the loss function, which is L1 in the figure,
the EMD between output and ground truth point clouds will also be large.
Hence, it makes sense to use the combination of CD and EMD, which is L3 in the figure,
which gives both small CD and EMD.
However, since calculating EMD is very expensive and time-consuming,
using CD to evaluate intermediate output $P_m$ and EMD to evaluate final output $P_o$, which is L4 in the figure,
is also a good choice for the sake of efficiency.
\begin{figure*}[htb!]
	\centerline{
		\begin{minipage}{0.99\linewidth}
			\begin{center}
				\setlength{\epsfxsize}{0.49\linewidth}
				\epsffile{figures/plots/loss_cd.eps}
				\setlength{\epsfxsize}{0.49\linewidth}
				\epsffile{figures/plots/loss_emd.eps}\\
				{}
			\end{center}
			\caption{Comparison of Different Loss Functions.
				L1: both $d_1$ and $d_2$ are CD; L2: both $d_1$ and $d_2$ are EMD; L3: both $d_1$ and $d_2$ are CD+EMD;
				L4: $d_1$ is CD and $d_2$ is EMD; L5: $d_1$ is EMD and $d_2$ is CD.}
			\label{loss_plot}
		\end{minipage}
	}
\end{figure*}

\noindent \textbf{Remarks}.
(1) CD and EMD are important metrics to evaluate the difference between two point clouds.
Generally speaking,
small CD/EMD value means better reconstruction performance, and  vice versa.
However,
due to the irregular format and lack-of-order information of point clouds,
these two metrics are quite limited in terms of accurately indicating the shape difference between two point clouds,
and hence cannot accurately describe the performance of a point cloud reconstruction method.
Sometimes, a reconstructed point cloud may have larger CD or EMD though its shape is more similar to the ground truth point cloud.
For example, as we can see in Fig. \ref{6methods_plot}, the EMD of chair using 3DRIMR is smaller than R2P\_EMD,
but from Fig. \ref{pc_plots}, we can see that the output of R2P\_EMD has more accurate shape of a chair.
Hence, we should not focus only on the values of CD or EMD when evaluating a method's reconstruction performance.
(2) We have also conducted a series of experiments to explore different designs of our network architecture,
e.g., using different pooling methods to extract global features, applying discriminators during the training process,
and deeper network architecture with more layers.
Nevertheless we find that the architecture shown in Fig. \ref{fig_G} performs the best and is efficient.

\section{CONCLUSIONS AND FUTURE WORK}\label{sec_conclusion}
We have proposed R2P, a deep learning model that generates 3D objects
in the form of smooth, dense, and highly accurate point clouds with fine geometry details.
The inputs to R2P are directly
converted from the 2D depth images that are generated from raw mmWave radar sensor data, and thus characterized by
mutual inconsistency or errors in terms of orientation and shape.
We have demonstrated with extensive experiments
that R2P significantly outperforms existing methods such as PointNet/PointNet++, PCN, and 3DRIMR.
In addition, we have shown the importance of loss function design
in the training of models for reconstructing point clouds.
For future work, we will further improve our design and test it with large scale experiments in more practical environments
and with more object categories.

 \bibliographystyle{splncs04}

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AAA.mcgrjame
The papers of arts educator James A. McGrath measure 5.3 linear feet and date from 1950-2011. Included are McGrath's papers concerning his artist's residencies and workshops for the United States Information Agency (USIA) in the Yemen Republic, Saudi Arabia, and the Republic of the Congo, 1990-1995. Also found are McGrath's papers concerning artist William Wiley. These papers date from Wiley's high school days and includes correspondence, writings, student files, printed materials, photographs, and artwork. Letters from Wiley to McGrath span several decades and provide details about his artwork, family, and travels.
Peter A. Juley & Son collection
Ward, De Witt
Peter A. Juley & Son
Juley, Peter A., 1862-1937
Juley, Paul, 1890-1975
700 Linear feet ((approx. 127,000 negatives, with black-and-white study prints))
SAAM.Photo.JUL
Smithsonian American Art Museum, Research and Scholars Center
The Peter A. Juley & Son Collection is comprised of approximately 127,000 photographic negatives documenting the work of more than 11,000 American artists. Included in the collection are 4,700 photographic portraits of artists.
Thomas Prichard Rossiter and Rossiter Family papers
Rossiter, Thomas Prichard, 1818-1871
AAA.rossthom
The Thomas Prichard Rossiter and Rossiter Family papers measure 0.5 linear feet and date from 1840 to 1961. Included are letters to painter Thomas Prichard Rossiter and letters to his son, architect Ehrick Kensett Rossiter, documenting their friendships with many artists and Thomas Prichard Rossiter's sketchbook and loose sketches. Edith Rossiter Bevan's papers include her writings on her grandfather, Thomas Prichard Rossiter; a scrapbook; photographs of the Rossiter family; notes by Bevan; news clippings; and other printed material. Also found is Bevan's collection of artists' letters.
Exhibition and Program Records
National Museum of American History. Office of Curatorial Affairs
This accession consists of records which document the oversight and review process at the National Museum of American History (NMAH) in regards to exhibitions, public programs, symposia, websites, productions, and performances. In particular, the activities of the Exhibitions and Program Committee (EPC) which was created in 1996. The EPC ...
Anna J. Cooper: a voice from the South exhibition records
Smithsonian Institution. Anacostia Community Museum
11.38 Linear feet (23 boxes)
An exhibition on Anna J. Cooper, Washington D. C. educator and author. It was organized by the Anacostia Neighborhood Museum and held there from February 1981 to September 1982. Louise Daniel Hutchinson served as curator. These records document the planning, organizing, execution, and promotion of the exhibition. Materials include correspondence, research files, exhibit scripts, administrative records, brochures, press coverage, education packets, loan agreements, and floor plans.
Publicity Records
National Portrait Gallery, Office of Public Affairs
14.04 linear meters.
This finding aid was digitized with funds generously provided by the Smithsonian Institution Women's Committee.
East of the River: Continuity and Change Exhibition Records
Anacostia Community Museum
2.75 Linear feet (4 boxes)
An exhibition to commemorate the 40th anniversary of the founding of the Anacostia Community Museum, formerly known as the Anacostia Neighborhood Museum, organized by the museum and held there September 15, 2007 through November 9, 2008. The exhibit explored the development of community life of neighborhoods east of the Anacostia River, beginning with the original inhabitation by Native Americans up to the present.

redpajama_set_name: RedPajamaCommonCrawl


The Elevator Studios website was created by the Smiling Wolf team. I was the Web Developer at Smiling Wolf and built the site. The design and idea was created by various members of the Smiling Wolf team.
Elevator studios say: "Elevator Studios has become Liverpool's creative hub, providing working space for Artists, Musicians and Commercial Companies."


text:
AAN Expands Impeachment Ad Blitz With $1.5 Million in New TV Ads
This morning, the American Action Network announced a new expansion of its advertising blitz against impeachment, enlarging its campaign with an additional $1.5 million of new television ads that will run across 10 Congressional Districts. The ads, which will begin running on cable and broadcast today as well as on digital and online media, highlight how the push to impeach President Trump is sidelining all progress on the issues that matter to folks back home.
The ads build on AAN's previously announced $7 million effort targeting impeachment and brings the campaign to a total of $8.5 million. Sample advertisements and list of districts can be viewed below.
"Americans expect Members of Congress to get to work on reducing health care costs, securing the border and creating better trade deals, but instead Congressional liberals have used their every waking moment to try and remove President Trump from office," said AAN President Dan Conston. "It's time for Members to get back to work, stop this impeachment nonsense and let the voters decide elections for themselves."
New ads similar to the above will run in the following districts:
Jared Golden (ME-02)
Susie Lee (NV-03)
Elissa Slotkin (MI-08)
Xochitl Torres Small (NM-02)
Anthony Brindisi (NY-22)
Kendra Horn (OK-05)
Joe Cunningham (SC-01)
Elaine Luria (VA-02)
In addition, AAN will be extending its ad buys thanking Congressman Rodney Davis (IL-13) and John Kato (NY-24) for standing against the impeachment charade and staying focused on the issues that matter. You can view those ads here and here.