Original: Self-Supervised Point Cloud Prediction Using 3D Spatio-Temporal Convolutional Networks
Revised: Self-Supervised Point Cloud Prediction Using 3D Spatio-Temporal Convolutional Networks

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AfﬁliationAddress email
Revised: AfﬁliationAddress email

Original: Abstract: Exploiting past 3D LiDAR scans to predict future point clouds is apromising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and planning.
Revised: Abstract: Exploiting past 3D LiDAR scans to predict future point clouds is apromising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and planning.

Original: In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDARscans.
Revised: In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDARscans.

Original: Estimating the future scene on the sensor level does not require any preceding steps as in localization or tracking systems and can be trained self-supervised.
Revised: Estimating the future scene on the sensor level does not require any preceding steps as in localization or tracking systems and can be trained self-supervised.

Original: We propose an end-to-end approach that exploits a 2D range image representationof each 3D LiDAR scan and concatenates a sequence of range images to obtaina 3D tensor.
Revised: We propose an end-to-end approach that exploits a 2D range image representationof each 3D LiDAR scan and concatenates a sequence of range images to obtaina 3D tensor.

Original: Based on such tensors, we develop an encoder-decoder architectureusing 3D convolutions to jointly aggregate spatial and temporal information of thescene and to predict the future 3D point clouds.
Revised: Based on such tensors, we develop an encoder-decoder architectureusing 3D convolutions to jointly aggregate spatial and temporal information of thescene and to predict the future 3D point clouds.

Original: We evaluate our method on multiple datasets and the experimental results suggest that our method outperformsexisting point cloud prediction architectures and generalizes well to new, unseenenvironments without additional ﬁne-tuning.
Revised: We evaluate our method on multiple datasets and the experimental results suggest that our method outperformsexisting point cloud prediction architectures and generalizes well to new, unseenenvironments without additional ﬁne-tuning.

Original: Our method operates online and isfaster than the common LiDAR frame rate of 10 Hz.
Revised: Our method operates online and isfaster than the common LiDAR frame rate of 10 Hz.

Original:  Introduction
Revised:  Introduction

Original: Sequential 3D point clouds obtained from Light Detection And Ranging (LiDAR) sensors are widelyused for numerous tasks in robotics and autonomous driving, like localization [1], detection [2, 3],mapping [4], segmentation [5, 6] and trajectory prediction [7].
Revised: Sequential 3D point clouds obtained from Light Detection And Ranging (LiDAR) sensors are widelyused for numerous tasks in robotics and autonomous driving, like localization [1], detection [2, 3],mapping [4], segmentation [5, 6] and trajectory prediction [7].

Original: The ability to forecast what thesensor is likely to see in the future can enhance decision-making for an autonomous vehicle.
Revised: The ability to forecast what thesensor is likely to see in the future can enhance decision-making for an autonomous vehicle.

Original: Apromising application is to use the predicted point clouds for path planning tasks like collisionavoidance.
Revised: Apromising application is to use the predicted point clouds for path planning tasks like collisionavoidance.

Original: In contrast to most approaches, which predict for example future 3D bounding boxes oftrafﬁc agents, point cloud prediction does not need any preceding inference steps such as localization, detection or tracking to predict a future scene.
Revised: In contrast to most approaches, which predict for example future 3D bounding boxes oftrafﬁc agents, point cloud prediction does not need any preceding inference steps such as localization, detection or tracking to predict a future scene.

Original: Running an off-the-shelf detection and trackingsystem on the predicted point clouds yields future 3D object bounding boxes as demonstrated byrecent approaches for point cloud forecasting [8, 9].
Revised: Running an off-the-shelf detection and trackingsystem on the predicted point clouds yields future 3D object bounding boxes as demonstrated byrecent approaches for point cloud forecasting [8, 9].

Original: From a machine learning perspective, pointcloud prediction is an interesting problem since the ground truth data is always given by the nextincoming LiDAR scans.
Revised: From a machine learning perspective, pointcloud prediction is an interesting problem since the ground truth data is always given by the nextincoming LiDAR scans.

Original: Thus, we can train our point cloud prediction self-supervised without theneed for expensive labeling and evaluate its performance online in unknown environments.
Revised: Thus, we can train our point cloud prediction self-supervised without theneed for expensive labeling and evaluate its performance online in unknown environments.

Original: In this paper, we address the problem of predicting large and unordered future point clouds from agiven sequence of past scans as shown in Fig.
Revised: In this paper, we address the problem of predicting large and unordered future point clouds from agiven sequence of past scans as shown in Fig.

Original: High dimensional and sparse 3D point cloud datarender point cloud prediction a challenging problem that is not yet fully explored in the literature.
Revised: High dimensional and sparse 3D point cloud datarender point cloud prediction a challenging problem that is not yet fully explored in the literature.

Original: Afuture point cloud can be estimated by applying a predicted future scene ﬂow to the last received scanor by generating a new set of future points.
Revised: Afuture point cloud can be estimated by applying a predicted future scene ﬂow to the last received scanor by generating a new set of future points.

Original: In this paper, we focus on the generation of new pointclouds to predict the future scene.
Revised: In this paper, we focus on the generation of new pointclouds to predict the future scene.

Original: In contrast to existing approaches [8, 9], which exploit recurrentneural networks for modeling temporal correspondences, we use 3D convolutions to jointly encodethe spatial and temporal information.
Revised: In contrast to existing approaches [8, 9], which exploit recurrentneural networks for modeling temporal correspondences, we use 3D convolutions to jointly encodethe spatial and temporal information.

Original: Our proposed approach takes a new 3D representation based onconcatenated range images as input and jointly estimates a future range image and per-point scoresfor being a valid or an invalid point for multiple future time steps.
Revised: Our proposed approach takes a new 3D representation based onconcatenated range images as input and jointly estimates a future range image and per-point scoresfor being a valid or an invalid point for multiple future time steps.

Original: This allows us to predict detailedfuture point clouds of varying sizes with a reduced number of parameters to optimize resulting infaster training and inference times.
Revised: This allows us to predict detailedfuture point clouds of varying sizes with a reduced number of parameters to optimize resulting infaster training and inference times.

Original: Furthermore, our approach is also fully self-supervised and doesnot require any manual labeling of the data.
Revised: Furthermore, our approach is also fully self-supervised and doesnot require any manual labeling of the data.

Original: Prediction
Revised: Prediction

Original: TT
Revised: T-P+ T

Original: T+ T+
Revised: T+ T+F

Original: The main contribution of this paper is a novel range image-based encoder-decoder neural network tojointly process spatio-temporal information from points clouds using 3D convolutions.
Revised: The main contribution of this paper is a novel range image-based encoder-decoder neural network tojointly process spatio-temporal information from points clouds using 3D convolutions.

Original: Our methodcan obtain structural details of the environment by using skip connections and horizontal consistencyusing circular padding, and in the end, provides more accurate predictions than other state-of-theart approaches for point cloud prediction.
Revised: Our methodcan obtain structural details of the environment by using skip connections and horizontal consistencyusing circular padding, and in the end, provides more accurate predictions than other state-of-theart approaches for point cloud prediction.

Original: We make three main key claims: Our approach is ableto (i) predict a sequence of detailed future 3D point clouds from a given input sequence by a fastjoint spatio-temporal point cloud processing using temporal 3D convolutional networks (CNNs),(ii) outperform state-of-the-art point cloud prediction approaches, (iii) generalize well to unseenenvironments, and operate online faster than a typical rotating 3D LiDAR sensor frame rate (10 Hz).
Revised: We make three main key claims: Our approach is ableto (i) predict a sequence of detailed future 3D point clouds from a given input sequence by a fastjoint spatio-temporal point cloud processing using temporal 3D convolutional networks (CNNs),(ii) outperform state-of-the-art point cloud prediction approaches, (iii) generalize well to unseenenvironments, and operate online faster than a typical rotating 3D LiDAR sensor frame rate (10 Hz).

Original: These claims are backed up by the paper and our experimental evaluation.
Revised: These claims are backed up by the paper and our experimental evaluation.

Original: Finally, we will releasethe code of our approach upon acceptance of the paper.
Revised: Finally, we will releasethe code of our approach upon acceptance of the paper.

Original:  Related Work
Revised:  Related Work

Original: Extracting spatio-temporal information from point cloud sequences has been exploited in the literature for different tasks related to point cloud prediction.
Revised: Extracting spatio-temporal information from point cloud sequences has been exploited in the literature for different tasks related to point cloud prediction.

Original: Liu et al.
Revised: Liu et al.

Original: [10] extend PointNet++ [6],which extracts spatial features from a single point cloud, and propose FlowNet3D for estimatingthe 3D motion ﬁeld between two point clouds.
Revised: [10] extend PointNet++ [6],which extracts spatial features from a single point cloud, and propose FlowNet3D for estimatingthe 3D motion ﬁeld between two point clouds.

Original: FlowNet3D is used by Lu et al.
Revised: FlowNet3D is used by Lu et al.

Original: [11] to interpolateintermediate frames between two LiDAR scans.
Revised: [11] to interpolateintermediate frames between two LiDAR scans.

Original: Other deep learning-based scene ﬂow estimationmethods can be found in the literature [12, 13].
Revised: Other deep learning-based scene ﬂow estimationmethods can be found in the literature [12, 13].

Original: In contrast to scene ﬂow between two past frames,point cloud prediction estimates future point clouds.
Revised: In contrast to scene ﬂow between two past frames,point cloud prediction estimates future point clouds.

Original: In addition to that, scene ﬂow requires labelslike point-wise correspondences or segmentation masks.
Revised: In addition to that, scene ﬂow requires labelslike point-wise correspondences or segmentation masks.

Original: This makes learning costly.
Revised: This makes learning costly.

Original: In contrast tothat, we focus on the self-supervised generation of new, future point clouds.
Revised: In contrast tothat, we focus on the self-supervised generation of new, future point clouds.

Original: Predicting future trajectories of trafﬁc participants based on their past motion is one possible application of LiDAR-based future scene prediction.
Revised: Predicting future trajectories of trafﬁc participants based on their past motion is one possible application of LiDAR-based future scene prediction.

Original: Given LiDAR scans only, these methods relyon preceding detection and tracking modules to infer past trajectories.
Revised: Given LiDAR scans only, these methods relyon preceding detection and tracking modules to infer past trajectories.

Original: For example, Luo et al.
Revised: For example, Luo et al.

Original: [7]develop a 3D convolutional architecture based on a bird’s eye view (BEV) of voxelized LiDARscans transformed by the sensor’s ego-motion.
Revised: [7]develop a 3D convolutional architecture based on a bird’s eye view (BEV) of voxelized LiDARscans transformed by the sensor’s ego-motion.

Original: Inspired by this, Casas et al.
Revised: Inspired by this, Casas et al.

Original: [14] increase the prediction horizon with IntentNet and also estimate a high-level driver behavior from HD maps.
Revised: [14] increase the prediction horizon with IntentNet and also estimate a high-level driver behavior from HD maps.

Original: Liang etal.
Revised: Liang etal.

Original: [15] integrate a tracking module into the end-to-end pipeline to improve the temporal consistency of the predictions while Meyer et al.
Revised: [15] integrate a tracking module into the end-to-end pipeline to improve the temporal consistency of the predictions while Meyer et al.

Original: [3] and Laddha et al.
Revised: [3] and Laddha et al.

Original: [16] explore the range imagerepresentation to predict future 3D bounding boxes.
Revised: [16] explore the range imagerepresentation to predict future 3D bounding boxes.

Original: A second method to estimate a future scenerepresentation without the need for prior object detections is prediction performed at the sensorlevel.
Revised: A second method to estimate a future scenerepresentation without the need for prior object detections is prediction performed at the sensorlevel.

Original: Hoermann et al.
Revised: Hoermann et al.

Original: [17] directly predict a future occupancy grid map from a sequence of past3D LiDAR scans and Song et al.
Revised: [17] directly predict a future occupancy grid map from a sequence of past3D LiDAR scans and Song et al.

Original: [18] predict a motion ﬂow for indoor 2D LiDAR maps.
Revised: [18] predict a motion ﬂow for indoor 2D LiDAR maps.

Original: Wu etal.
Revised: Wu etal.

Original: [19] use a spatio-temporal pyramid network to estimate voxelized BEV classiﬁcation and future motion maps.
Revised: [19] use a spatio-temporal pyramid network to estimate voxelized BEV classiﬁcation and future motion maps.

Original: Recently, Toyungyernsub et al.
Revised: Recently, Toyungyernsub et al.

Original: [20] differentiate between dynamic and staticcells for grid map prediction but require tracking labels to segment moving cells.
Revised: [20] differentiate between dynamic and staticcells for grid map prediction but require tracking labels to segment moving cells.

Original: In contrast to theaforementioned approaches, our method operates on the full-size point clouds without the need forvoxelization.
Revised: In contrast to theaforementioned approaches, our method operates on the full-size point clouds without the need forvoxelization.

Original: As shown by Weng et al.
Revised: As shown by Weng et al.

Original: [8], a reversed trajectory prediction pipeline can take thepoint cloud predictions and detect and track objects in future scans.
Revised: [8], a reversed trajectory prediction pipeline can take thepoint cloud predictions and detect and track objects in future scans.

Original: This makes it possible to ﬁrsttrain the prediction of point clouds self-supervised and then use off-the-shelf detection and trackingmodules to get future trajectories.
Revised: This makes it possible to ﬁrsttrain the prediction of point clouds self-supervised and then use off-the-shelf detection and trackingmodules to get future trajectories.

Original: There has been a large interest in vision-based prediction.
Revised: There has been a large interest in vision-based prediction.

Original: Srivastava et al.
Revised: Srivastava et al.

Original: [21] proposean LSTM [22] encoder-decoder model that takes an input sequence of image patches or featurevectors and outputs the sequence of future patches or feature vectors, respectively.
Revised: [21] proposean LSTM [22] encoder-decoder model that takes an input sequence of image patches or featurevectors and outputs the sequence of future patches or feature vectors, respectively.

Original: Shi et al.
Revised: Shi et al.

Original: [23]propose ConvLSTMs that employ convolutional structures in the temporal transitions imposed bythe LSTM.
Revised: [23]propose ConvLSTMs that employ convolutional structures in the temporal transitions imposed by

Original: This makes it possible to jointly encode temporal and spatial correlations in image se-

Original: Spatio-Temporal
Revised: Spatio-Temporal

Original: [Figure]
Revised: [Figure]

Original: ˆ S T + [Figure]
Revised: ˆ S T + the LSTM.

Original: quences.
Revised: This makes it possible to jointly encode temporal and spatial correlations in image sequences.

Original: The integration of 3D convolutions into LSTMs has been researched by Wang et al.
Revised: The integration of 3D convolutions into LSTMs has been researched by Wang et al.

Original: Aigner et al.
Revised: Aigner et al.

Original: [25] omit the use of recurrent structures and develop a video prediction approachcalled FutureGAN based on 3D convolutions combined with a progressively growing GAN.
Revised: [25] omit the use of recurrent structures and develop a video prediction approachcalled FutureGAN based on 3D convolutions combined with a progressively growing GAN.

Original: Usingthe range image representation of LiDAR scans, we can transfer the techniques from vision-basedmethods for solving point cloud prediction.
Revised: Usingthe range image representation of LiDAR scans, we can transfer the techniques from vision-basedmethods for solving point cloud prediction.

Original: However, there are signiﬁcant differences between videoprediction and range image-based point cloud prediction.
Revised: However, there are signiﬁcant differences between videoprediction and range image-based point cloud prediction.

Original: The main difference is that range imagescontain 3D point information rather than RGB values.
Revised: The main difference is that range imagescontain 3D point information rather than RGB values.

Original: In addition to that, range images have a 360degrees ﬁeld of view, which causes a strong correlation between the left and right image bordersbecause of the principle of a typical rotating LiDAR sensor, e.g., a Velodyne or an Ouster scanner.
Revised: In addition to that, range images have a 360degrees ﬁeld of view, which causes a strong correlation between the left and right image bordersbecause of the principle of a typical rotating LiDAR sensor, e.g., a Velodyne or an Ouster scanner.

Original: Though vision-based and voxel-based prediction methods have been well studied, predicting fullscale future point clouds is still challenging and has not yet been fully explored in existing works.
Revised: Though vision-based and voxel-based prediction methods have been well studied, predicting fullscale future point clouds is still challenging and has not yet been fully explored in existing works.

Original: To predict a future sequence of unordered and unstructured point clouds, Fan et al.
Revised: To predict a future sequence of unordered and unstructured point clouds, Fan et al.

Original: [26] propose apoint recurrent neural network (PointRNN).
Revised: [26] propose apoint recurrent neural network (PointRNN).

Original: Their method models spatio-temporal local correlationsof point features and states.
Revised: Their method models spatio-temporal local correlationsof point features and states.

Original: Lu et al.
Revised: Lu et al.

Original: [9] introduce MoNet, which integrates a point-based contextand motion encoder network and processes these features in a novel recurrent neural network.
Revised: [9] introduce MoNet, which integrates a point-based contextand motion encoder network and processes these features in a novel recurrent neural network.

Original: A3D scene ﬂow-based point cloud prediction approach has been recently developed by Deng and
Revised: A3D scene ﬂow-based point cloud prediction approach has been recently developed by Deng and

Original: Zakhor [27].
Revised: Zakhor [27].

Original: All three methods are 3D point-based but only consider a limited number of points.
Revised: All three methods are 3D point-based but only consider a limited number of points.

Original: Weng et al.
Revised: Weng et al.

Original: [8] propose an encoder-decoder network that takes a highly compressed feature representation of a full-scale LiDAR scan and models the temporal correlations with a recurrent autoencoder.
Revised: [8] propose an encoder-decoder network that takes a highly compressed feature representation of a full-scale LiDAR scan and models the temporal correlations with a recurrent autoencoder.

Original: The feature vectors are obtained either by a point-based architecture or 2D convolutionalneural networks processing 2D range image representations.
Revised: The feature vectors are obtained either by a point-based architecture or 2D convolutionalneural networks processing 2D range image representations.

Original: Deep learning-based generation of LiDAR scans from a 2D range image has been investigated by Caccia et al.
Revised: Deep learning-based generation of LiDAR scans from a 2D range image has been investigated by Caccia et al.

Original: [28] as well.
Revised: [28] as well.

Original: In contrast toexisting approaches, we propose a technique that concatenates the projected 2D range images to aspatio-temporal 3D representation, which makes it possible to use 3D convolutions without the needfor recurrent architectures for temporal modeling.
Revised: In contrastto existing approaches, we propose a technique that concatenates the projected 2D range images toa spatio-temporal 3D representation, which makes it possible to use 3D convolutions without theneed for recurrent architectures for temporal modeling.

Revised: As discussed by Bai et al.

Revised: [29] for generalsequence modeling, using convolutional networks results in an architecture that is easier to train andfaster at inference since the input range images are no longer processed sequentially.

Original: Compared to MoNet [9], this allows us to predictmore future point clouds in a shorter amount of time.
Revised: Compared to MoNet [9], this allows us to predict more future point clouds in a shorter amount of time.

Original:  Our Approach
Revised:  Our Approach

Original: The goal of our approach is to predict a future sequence of full-scale LiDAR points given a sequenceof past scans.
Revised: The goal of our approach is to predict a future sequence of full-scale LiDAR points given a sequenceof past scans.

Original: As illustrated in the overview of our method in Fig.
Revised: As illustrated in the overview of our method in Fig.

Original: 2, we ﬁrst project each past3D point cloud into a range image representation and concatenate the 2D images to obtain a 3Dspatio-temporal tensor, see also Appendix A for details.
Revised: 2, we ﬁrst project each past3D point cloud into a range image representation and concatenate the 2D images to obtain a 3Dspatio-temporal tensor, see also Appendix A for details.

Original: Second, we pass this tensor to our encoderdecoder architecture to extract the spatial and temporal correlations with 3D convolutional kernelsas described in Sec. 3.1.
Revised: Second, we pass this tensor to our encoderdecoder architecture to extract the spatial and temporal correlations with 3D convolutional kernelsas described in Sec. 3.1.

Original: We use skip connections and circular padding to maintain details andhorizontal consistency of the predicted range images, see Sec. 3.2.
Revised: We use skip connections and circular padding to maintain details andhorizontal consistency of the predicted range images, see Sec. 3.2.

Original: Details on the training procedureare provided in Sec.
Revised: Details on the training procedureare provided in Sec.

Original: 3.1 Spatio-Temporal Encoder-Decoder Architecture
Revised: 3.1 Spatio-Temporal Encoder-Decoder Architecture

Original: Our approach uses a spherical projection to get a dense 2D image-like view of a LiDAR scan fromwhich 3D information can still be recovered.For technical details on the range image projection [Figure] [Figure]
Revised: Our approach ﬁrst converts the 3D LiDAR points into spherical coordinates and maps them to imagecoordinates resulting in a dense 2D range image from which 3D information can still be recovered.

Original: i [Figure] t
Revised: Ifno point is projected into a speciﬁc pixel, its value is set to zero.

Original: [Figure] R a ng e P r e d i c t e d M a s k
Revised: If multiple points are mapped to thesame pixel, we keep the closest point to focus on the prediction of foreground objects.

Original: L og it s S t ac k e d R a ng e I m a g e s see Appendix A.
Revised: For technicaldetails on the range image projection see Appendix A.

Original: The main task of the encoder-decoder architecture illustrated in Fig.
Revised: The main task of the encoder-decoder architecture illustrated in Fig.

Original: 3 is to jointlyextract spatio-temporal features from the input sequence and to output future range view predictions.
Revised: 3 is to jointly extract spatio-temporal features from the input sequenceand to output future range image predictions.

Revised: Compared to a multilayer perceptron architecturethat disregards spatial and temporal dimensions, the use of convolutions imposes an inductive biasof having local correlations along these dimensions.

Revised: Convolutional networks usually require lesstrainable parameters and are less prone to overﬁtting.

Original: When using range images for point cloud prediction with 3D convolutions,a sufﬁcient receptive ﬁeldbetween encoder and decoder is required to capture the motion of points in the past frames and topropagate their locations into the future range images.
Revised: When using range images for point cloud prediction with 3D convolutions, sufﬁcient receptive ﬁelds along the spatial and temporal dimensionsbetween encoder and decoder are required to capture the motion of points in the past frames and topropagate their locations into the future range images.

Original: The encoder takes the 3D input tensor of size ( P, H, W )  and ﬁrst standardizes the range valuesbased on mean and standard deviation computed from the training data.
Revised: The encoder takes the 3D input tensor of size ( P, H, W ) , containing P range images with height Hand width W , and ﬁrst standardizes the range values based on mean and standard deviation computed from the training data.

Original: We pass the standardized tensor through a 3D convolutional input layer with C kernels to get a C -channel featurerepresentation of size ( C, P, H, W ) .
Revised: We pass the standardized tensor through a 3D convolutional inputlayer with C kernels to get a C -channel feature representation of size ( C, P, H, W ) .

Original: Inspired by FutureGAN [25], the encoder block receives atensor of size ( C l , P l , H l , W l ) at each encoder stage l , applies a combination of 3D convolution,3D batch normalization (BN) [29], and leaky ReLU activation function [30] while maintainingthe size of the tensor.
Revised: Inspired by FutureGAN [25], the encoder block receives a tensor of size ( C l , P l , H l , W l ) at each encoder stage l ,applies a combination of 3D convolution, 3D batch normalization (BN) [30], and leaky ReLU activation function [31] while maintaining the size of the tensor.

Original: A strided 3D convolution follows resulting in a downsampled tensor ofsize ( C l +1 , P l − p l , H l h l , W l w l ) where h l and w l are predeﬁned downsampling factors for the spatialfeature size and p l reduces the temporal feature dimension.
Revised: A strided 3D convolution followsresulting in a downsampled tensor of size ( C l +1 , P l − p l , H l h l , W l w l ) where h l and w l are predeﬁneddownsampling factors for the spatial feature size and p l reduces the temporal feature dimension.

Original: We batch normalize the resulting tensorand apply a leaky ReLU activation function.
Revised: We batch normalize the resulting tensor and apply a leaky ReLU activation function.

Original: The kernel size is ( p l , h l , w l ) and the stride (1 , h l , w l )to achieve the desired downsampling.
Revised: The kernelsize is ( p l , h l , w l ) and the stride (1 , h l , w l ) to achieve the desired downsampling.

Original: The downsampling compresses the sequential point cloudfeature representation and forces the network to learn meaningful spatio-temporal features.
Revised: The downsampling compresses the sequential point cloud feature representation and forces the network to learnmeaningful spatio-temporal features.

Original: To predict future range images for F future time steps, the decoder subsequently upsamples thefeature tensor to the ﬁnal output size (2 , F, H, W ) .
Revised: To predict future range images for F future time steps, the decoder subsequently upsamples thefeature tensor to the ﬁnal output size (2 , F, H, W ) .

Revised: Note that the number of future range imagesis ﬁxed in the architecture, but longer prediction horizons can be achieved in an auto-regressivemanner.

Revised: This is done by sequentially feeding back the predicted range images as the input tensor.

Revised: In this work, we will only focus on the prediction of a ﬁxed number of future point clouds.

Original: The decoder architecture is a mirrored version of the encoder.
Revised: Thedecoder architecture is a mirrored version of the encoder.

Original: First, a transposed 3D convolutional layer with kernel size ( p l , h l , w l ) andstride (1 , h l , w l ) increase the feature map.
Revised: First, a transposed 3D convolutional layerwith kernel size ( p l , h l , w l ) and stride (1 , h l , w l ) increase the feature map.

Original: We insert a 3D batch normalization and leaky ReLU activation layer before a second 3D CNN, 3D BN, and leaky ReLU follows.
Revised: We insert a 3D batchnormalization and leaky ReLU activation layer before a second 3D CNN, 3D BN, and leaky ReLUfollows.

Original: Finally we pass the outputtensor of size ( C, F, H, W ) through a 3D CNN output layer with two kernels of size (1 , 1 , 1) andstride (1 , 1 , 1) and apply the ﬁnal Sigmoid function to get normalized values between 0 and 1.
Revised: Finally we pass the output tensor of size ( C, F, H, W ) through a 3D CNN output layerwith two kernels of size (1 , 1 , 1) and stride (1 , 1 , 1) and apply the ﬁnal Sigmoid function to getnormalized values between 0 and 1.

Original: Theﬁrst output channel maps to a predeﬁned range interval resulting in the future range predictions.
Revised: The ﬁrst output channel maps to a predeﬁned range intervalresulting in the future range predictions.

Original: Asdone by Weng et al.
Revised: As done by Weng et al.

Original: [8], the second channel contains a probability for each range image point to bea valid point for re-projection.
Revised: [8], the second channel contains aprobability for each range image point to be a valid point for re-projection.

Original: The re-projection mask keeps all points with a probability above 0.5.
Revised: The re-projection maskkeeps all points with a probability above 0.5.

Original: This makes it possible to mask out e.g. the sky since there are no ground truth points available.
Revised: This makes it possible to mask out e.g. the sky sincethere are no ground truth points available.

Original: 3.2 Skip Connections and Horizontal Circular Padding
Revised: 3.2 Skip Connections and Horizontal Circular Padding

Original: We use strided 3D convolutions and downsample the range images as described in Sec. 3.1.
Revised: We use strided 3D convolutions and downsample the range images as described in Sec. 3.1.

Original: Thereduced size of the feature space however causes a loss of details in the predicted range images.
Revised: Thereduced size of the feature space however causes a loss of details in the predicted range images.

Original: Weaddress this problem by adding skip connections [31] between the encoder and decoder to maintaindetails from the input scene.
Revised: Weaddress this problem by adding skip connections [32] between the encoder and decoder to maintaindetails from the input scene.

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 3, the feature maps bypass the remaining encodingsteps and the mirrored decoder stage concatenates them with the previously upsampled feature vol- ume along the channel dimension.
Revised: 3, the feature maps bypass the remaining encodingsteps and the mirrored decoder stage concatenates them with the previously upsampled feature volume along the channel dimension.

Original: Concatenation enables the network to account for the temporaloffset between encoder and decoder feature maps.
Revised: Concatenation enables the network to account for the temporaloffset between encoder and decoder feature maps.

Original: A combination of 3D convolutions, 3D batch normalization, and leaky ReLU follows to merge the features back to the original number of channelswhile maintaining the temporal and spatial dimensions.
Revised: A combination of 3D convolutions, 3D batch normalization, and leaky ReLU follows to merge the features back to the original number of channelswhile maintaining the temporal and spatial dimensions.

Original: We investigate the effect of skip connectionsin Sec.
Revised: We investigate the effect of skip connectionsin Sec.

Original: 4.1 and Sec. 4.4 and show that they maintain details in the predicted point clouds. 
Revised: 4.1 and Sec. 4.4 and show that they maintain details in the predicted point clouds. [Figure] [Figure] i

Revised: [Figure]

Revised: t

Original: Another challenge when using range images for 3D point cloud prediction is to maintain spatial consistency on the horizontal borders of the range images.
Revised: [Figure] R a ng e P r e d i c t e d M a s k

Revised: L og it s

Revised: S t ac k e d R a ng e I m a g e s viewed in color.

Revised: Another challenge when using range images for 3D point cloud prediction is to maintain spatial consistency on the horizontal borders of the range images.

Original: Range images obtained by rotating LiDARsensors such as a Velodyne or an Ouster sensor, are panoramic images and have strong horizontalcorrelations between the image borders.
Revised: Range images obtained by rotating LiDARsensors such as a Velodyne or an Ouster sensor, are panoramic images and have strong horizontalcorrelations between the image borders.

Original: If the ego vehicle is rotating along the vertical z axis, anobject passing the left border of the range image will appear on the right border.
Revised: If the ego vehicle is rotating along the vertical z axis, anobject passing the left border of the range image will appear on the right border.

Original: To take this property into account, we introduce circular padding for the width dimension.
Revised: To take this property into account, we introduce circular padding for the width dimension.

Original: We pad the left side ofeach feature map with its right side and vice versa.
Revised: We pad the left side ofeach feature map with its right side and vice versa.

Original: The vertical dimension is padded with zeros.
Revised: The vertical dimension is padded with zeros.

Original: Anexperiment on the effectiveness of this padding is provided in Sec. 4.1.
Revised: Anexperiment on the effectiveness of this padding is provided in Sec. 4.1.

Original: 3.3 Training
Revised: 3.3 Training

Original: When training the network, we project the ground truth point clouds into the range images ofsize ( H, W ) , which allows for the computation of 2D image-based losses.
Revised: When training the network, we project the ground truth point clouds into the range images ofsize ( H, W ) , which allows for the computation of 2D image-based losses.

Original: We slice the data intosamples of sequences consisting of P past and F future frames, where subsequent samples are oneframe apart.
Revised: We slice the data intosamples of sequences consisting of P past and F future frames, where subsequent samples are oneframe apart.

Original: Our architecture is trained with a combination of multiple losses.
Revised: Our architecture is trained with a combination of multiple losses.

Original: The average rangeloss L R ,t at time step t between a predicted range image (ˆ r i,j ) t ∈ R H × W and a ground truth rangeimage ( r i,j ) t ∈
Revised: The average rangeloss L R ,t at time step t between a predicted range image (ˆ r i,j ) t ∈ R H × W and a ground truth rangeimage ( r i,j ) t ∈

Original: R
Revised: R

Original: H
Revised: H

Original: × W is given by
Revised: × W is given by

Original: L R ,t =
Revised: L R ,t =

Original: HW (cid:88) i,j ∆ r i,j , with ∆ r i,j = (cid:26) (cid:107)
Revised: HW (cid:88) i,j ∆ r i,j , with ∆ r i,j = (cid:26) (cid:107)

Original: ˆ r
Revised: ˆ r

Original: i,j − r i,j (cid:107), if r i,j > , otherwise , (1) such that the range loss is only computed for valid ground truth points.
Revised: i,j − r i,j (cid:107), if r i,j > , otherwise , (1) such that the range loss is only computed for valid ground truth points.

Original: We train the mask output attime step t with predicted probabilities ( ˆ m i,j ) t ∈ R H × W with a binary cross-entropy loss 
Revised: We train the mask output attime step t with predicted probabilities ( ˆ m i,j ) t ∈ R H × W with a binary cross-entropy loss L M ,t =

Revised: HW (cid:88)

Original: [Equation]
Revised: [Equation]

Original: where y i,j is 1 in case the ground truth point is valid and 0 otherwise.
Revised: where y i,j is 1 in case the ground truth point is valid and 0 otherwise.

Original: Both losses only considerthe predicted 2D range images and not the re-projected point clouds to compute the loss faster.
Revised: Both losses only considerthe predicted 2D range images and not the re-projected point clouds to compute the loss faster.

Original: Forcomparison to 3D-based methods like MoNet [9], we ﬁne-tune our model on a 3D point-based loss.
Revised: Forcomparison to 3D-based methods like MoNet [9], we ﬁne-tune our model on a 3D point-based loss.

Original: A common metric for comparing 3D point clouds is the Chamfer distance [32].
Revised: A common metric for comparing 3D point clouds is the Chamfer distance [33].

Original: At time t , we reproject the masked range image into a 3D point cloud ˆ S t = { ˆ p ∈ R 3 } , | ˆ S t | = N, and compare itwith the ground truth point cloud S t
Revised: At time t , we reproject the masked range image into a 3D point cloud ˆ S t = { ˆ p ∈ R 3 } , | ˆ S t | = N, and compare itwith the ground truth point cloud S t

Original: = { p ∈ R 3 } , |S t | =
Revised: = { p ∈ R 3 } , |S t | =

Original: M, by computing
Revised: M, by computing

Original: [Equation]
Revised: [Equation]

Original: The computation of the Chamfer distance is in general slow due to the search for nearest neighbors.
Revised: The computation of the Chamfer distance is in general slow due to the search for nearest neighbors.

Original: A fast to compute image-based loss using L R ,t and L M ,t is about 2.5 times faster compared to a lossincluding the 3D Chamfer distance.
Revised: A fast to compute image-based loss using L R ,t and L M ,t is about 2.5 times faster compared to a loss including the 3D Chamfer distance.

Original: We propose a training scheme with only range and mask lossfor pre-training.
Revised: We propose a training scheme with only range and mask lossfor pre-training.

Original: This results in a good initialization for ﬁne-tuning including a Chamfer distanceloss.
Revised: This results in a good initialization for ﬁne-tuning including a Chamfer distanceloss.

Original: We provide an experiment on the training scheme in Sec. 4.4.
Revised: We provide an experiment on the training scheme in Sec. 4.4.

Original: Given a current time step t = T ,our total loss function for F future time steps is given by
Revised: Given a current time step t = T ,our total loss function for F future time steps is given by

Original: [Equation]
Revised: [Equation]

Revised: with tunable weighting parameters α M and α CD .

Original: {+During the experiments, we experienced that settingα M = 1 . 
+}
Revised: {+During the experiments, we experienced that settingα M = 1 . 
+}

Original: with weight w = 0 {+. 
+} for pre-training and w = 1 {+. 
 and α CD = 1 . 0+} for ﬁne-tuning.
Revised: and α CD = 0 {+. 
+} for pre-training and α M = 1 {+. 
 and α CD = 1 . 0+} for ﬁne-tuning workedbest.

Original: We train our network parameterswith the Adam optimizer [33], a learning rate of 10 − 3 , and accumulate the gradients for 16 samples.
Revised: We train our network parameters with the Adam optimizer [34], a learning rate of 10 − 3 , andaccumulate the gradients for 16 samples.

Original: We use an exponential decaying learning rate schedule.
Revised: We use an exponential decaying learning rate schedule.

Original: All models require less than 50 epochs toconverge.
Revised: All models require less than 50 epochs to converge.

Original: [Figure]

Original:  Experimental Evaluation
Revised:  Experimental Evaluation

Original: In this work, we present an approach for self-supervised point cloud prediction using spatio-temporal3D convolutional neural networks.
Revised: In this work, we present an approach for self-supervised point cloud prediction using spatio-temporal3D convolutional neural networks.

Original: We present our experiments to show the capabilities of ourmethod and to support our key claims, which are: (i) predicting a sequence of detailed future 3Dpoint clouds from a given input sequence by a fast joint spatio-temporal point cloud processingusing temporal 3D convolutional networks, (ii) outperforming state-of-the-art point cloud predictionapproaches, (iii) generalizing well to unseen environments, and operate online faster than a typical rotating 3D LiDAR sensor frame rate of 10 Hz.
Revised: We present our experiments to show the capabilities of ourmethod and to support our key claims, which are: (i) predicting a sequence of detailed future 3Dpoint clouds from a given input sequence by a fast joint spatio-temporal point cloud processingusing temporal 3D convolutional networks, (ii) outperforming state-of-the-art point cloud predictionapproaches, (iii) generalizing well to unseen environments, and operate online faster than a typicalrotating 3D LiDAR sensor frame rate of 10 Hz.

Original: We follow the experimental setting of MoNet [9]with the KITTI Odometry dataset [34] and predict the next 5 frames from the past 5 LiDAR scanscaptured at 10 Hz.
Revised: We follow the experimental setting of MoNet [9]with the KITTI Odometry dataset [35] and predict the next 5 frames from the past 5 LiDAR scanscaptured at 10 Hz.

Original: Please see Appendix B for further details on the experimental setting.
Revised: Please see Appendix B for further details on the experimental setting.

Original: 4.1 Qualitative Results
Revised: 4.1 Qualitative Results

Original: The ﬁrst experiment supports our claim that our model can predict a sequence of detailed future3D point clouds using a temporal convolutional network and demonstrate the effectiveness of ourproposed skip connections and circular padding.
Revised: The ﬁrst experiment supports our claim that our model can predict a sequence of detailed future3D point clouds using a temporal convolutional network and demonstrate the effectiveness of ourproposed skip connections and circular padding.

Original: The situation in Fig.
Revised: The situation in Fig.

Original: 4 from test sequence 08 showsan ego-vehicle ﬁnishing a left turn while passing a close vehicle.
Revised: 4 from test sequence 08 showsan ego-vehicle ﬁnishing a left turn while passing a close vehicle.

Original: This is a challenging scenariobecause of the different scales of close and far objects and the ego-motion of the car.
Revised: This is a challenging scenariobecause of the different scales of close and far objects and the ego-motion of the car.

Original: Our approachpre-trained on range and mask loss only is capable of predicting the future point clouds across 5time steps and maintains details in the scene.
Revised: Our approachpre-trained on range and mask loss only is capable of predicting the future point clouds across 5time steps and maintains details in the scene.

Original: Without skip connections, small objects are lost dueto the compression (green circle).
Revised: Without skip connections, small objects are lost dueto the compression (green circle).

Original: With typical zero padding, the points belonging to the car in theback of the ego vehicle (black circle) are not consistently predicted since they span across the rangeimage borders, while our approach can solve this problem by using the proposed circular padding.
Revised: With typical zero padding, the points belonging to the car in theback of the ego vehicle (black circle) are not consistently predicted since they span across the rangeimage borders, while our approach can solve this problem by using the proposed circular padding.

Original: 4.2 Quantitative Results
Revised: 4.2 Quantitative Results

Original: To support the claim that our method outperforms state-of-the-art point cloud prediction approaches,we compare the quantitative results to multiple baselines.
Revised: To support the claim that our method outperforms state-of-the-art point cloud prediction approaches,we compare the quantitative results to multiple baselines.

Original: Note that Lu et al.
Revised: Note that Lu et al.

Original: [9] operate and alsoevaluate on sub-sampled point clouds, whereas our approach can predict full-size point clouds.
Revised: [9] operate and alsoevaluate on sub-sampled point clouds, whereas our approach can predict full-size point clouds.

Original: Toaccount for this, Tab. 1 ( Left ) shows the performance of our method on sampled point clouds withtwo different sampling rates used in MoNet [9].
Revised: Toaccount for this, Tab. 1 ( Left ) shows the performance of our method on sampled point clouds withtwo different sampling rates used in MoNet [9].

Original: For both samplings, our method achieves a betterresult for larger prediction steps.
Revised: For both samplings, our method achieves a betterresult for larger prediction steps.

Original: However, it can be seen that sampling points from the original pointclouds during evaluation strongly affects the Chamfer distance of our full-sized prediction.
Revised: However, it can be seen that sampling points from the original pointclouds during evaluation strongly affects the Chamfer distance of our full-sized prediction.

Original: Thus, we [Table] provide an evaluation on full-scale point clouds to demonstrate the true performance of our approach,see Tab.[-
-]
Revised: Thus, weprovide an evaluation on full-scale point clouds to demonstrate the true performance of our approach,see Tab.[-
-]

Original: ( Right ).
Revised: ( Right ).

Original: We compare our results to an Identity baseline that takes the last received scanas a constant prediction for all future time steps.
Revised: We compare our results to an Identity baseline that takes the last received scanas a constant prediction for all future time steps.

Original: The Constant Velocity and Ray Tracing baselinesestimate the transformation between the last two received scans with a SLAM approach [35] as aconstant velocity prediction of the sensor.
Revised: The Constant Velocity and Ray Tracing baselinesestimate the transformation between the last two received scans with a SLAM approach [36] as aconstant velocity prediction of the sensor.

Original: At each prediction step, the Constant Velocity baselinetransforms the last received point cloud according to the predicted motion.
Revised: At each prediction step, the Constant Velocity baselinetransforms the last received point cloud according to the predicted motion.

Original: The Ray Tracing baselineaggregates all ﬁve transformed past point clouds to render a more dense range image.
Revised: The Ray Tracing baselineaggregates all ﬁve transformed past point clouds to render a more dense range image.

Original: Since a lot ofpoints in the scene are static, these are very strong baselines that work well for constant ego-motion.
Revised: Since a lotof points in the scene are static, these are very strong baselines that work well for constant egomotion.

Original: Note that in contrast to our method, all three baselines do not consider points belonging to movingobjects.
Revised: Note that in contrast to our method, all three baselines do not consider points belongingto moving objects.

Original: The results in Tab. 1 show that our method outperforms all baselines at larger predictionsteps and that it can reason about the sensor’s ego-motion and the motion of moving points.
Revised: The results in Tab. 1 show that our method outperforms all baselines at larger Ours (Pre-trained) No Skip Connections No Circular Padding T+

Original: 4.3 Runtime
Revised: T+ T+ T+ T+

Revised: T+

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: Baseline

Revised: Baseline

Revised: Baseline

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: prediction steps and that it can reason about the sensor’s ego-motion and the motion of movingpoints.

Revised: The standard deviation of the Chamfer distance indicates that the results of our approach aremore consistent throughout the test set whereas the baseline results suffer from outliers in case ofmoving objects or non-linear ego-motion.

Revised: 4.3 Runtime

Original: For the runtime, our approach takes on average 11 ms (90 Hz) for predicting 5 future pointclouds with up to 131,072 points each on a system with an Intel i7-6850K CPU and an
Revised: For the runtime, our approach takes on average 11 ms (90 Hz) for predicting 5 future pointclouds with up to 131,072 points each on a system with an Intel i7-6850K CPU and an

Original: Nvidia GeForce RTX 2080 Ti GPU, which is faster than the sensor frame rate, i.e. 10 Hz, of a typicalrotating 3D LiDAR sensor and faster than existing approaches like MoNet [9] using the same GPU.
Revised: Nvidia GeForce RTX 2080 Ti GPU, which is faster than the sensor frame rate, i.e. 10 Hz, of a typicalrotating 3D LiDAR sensor and faster than existing approaches like MoNet [9] using the same GPU.

Original: Compared to a runtime of 280 ms reported by Lu et al.
Revised: Compared to a runtime of 280 ms reported by Lu et al.

Original: [9] for MoNet for predicting 5 future pointclouds with 65,536 points each, our approach is 25 times faster for twice as many points.
Revised: [9] for MoNet for predicting 5 future pointclouds with 65,536 points each, our approach is 25 times faster for twice as many points.

Original: 4.4 Ablation Study
Revised: 4.4 Ablation Study

Original: In this experiment, we provide a more detailed analysis of the effect of the model architecture andthe proposed training scheme.
Revised: In this experiment, we provide a more detailed analysis of the effect of the model architecture andthe proposed training scheme.

Original: We average the metrics over all validation samples and time stepsas shown in Tab. 2.
Revised: We average the metrics over all validation samples and time stepsas shown in Tab. 2.

Original: Note that we always report the Chamfer distance but it is only used duringtraining if speciﬁed.
Revised: Note that we always report the Chamfer distance but it is only used duringtraining if speciﬁed.

Original: First, we modify the size of the network by decreasing ( Small ) or increasing( Large ) the number of channels of our model by a factor of 2.
Revised: First, we modify the size of the network by decreasing ( Small ) or increasing ( Large ) the number of channels of our model by a factor of 2.

Original: The larger model predicts slightlybetter masked range images but also requires 4 times more parameters.
Revised: The larger model predicts slightlybetter masked range images but also requires 4 times more parameters.

Original: All losses increase withoutskip connections ( No Skip Connections ).
Revised: All losses increase withoutskip connections ( No Skip Connections ).

Original: This is because a lot of objects are not predicted due tothe large feature compression.
Revised: This is because a lot of objects are not predicted due tothe large feature compression.

Original: The effect of circular padding ( No Circular Padding ) is not visiblequantitatively because of the small number of affected points and we refer to Sec.
Revised: The effect of circular padding ( No Circular Padding ) is not visiblequantitatively because of the small number of affected points and we refer to Sec.

Original: 4.1 for a qualitativeevaluation.
Revised: 4.1 for a quali- [Table] tative evaluation.

Original: Finally, we show that our ﬁne-tuned model ( Ours ) achieves similar performance on thevalidation set compared to a model optimized with the Chamfer distance loss from the beginning( With CD  ).
Revised: Finally, we show that our ﬁne-tuned model ( Ours ) achieves similar performanceon the validation set compared to a model optimized with the Chamfer distance loss from the beginning ( CD Loss From Start ).

Original: Our proposed training scheme with pre-training on fast-to-compute image-based lossesgives a good pre-trained model for ﬁne-tuning with the 3D Chamfer distance loss while reducingtraining time.
Revised: Our proposed training scheme with pre-training on fast-to-computeimage-based losses gives a good pre-trained model for ﬁne-tuning with the 3D Chamfer distanceloss while reducing training time.

Original: 4.5 Generalization
Revised: 4.5 Generalization

Original: Finally, we aim at supporting our claim that our method generalizes well to new, unseen environments.
Revised: Finally, we aim at supporting our claim that our method generalizes well to new, unseen environments.

Original: We ﬁrst evaluate a model ﬁne-tuned on KITTI Odometry on the Apollo-SouthBay ColumbiaPark [36] test set.
Revised: We ﬁrst evaluate a model ﬁne-tuned on KITTI Odometry on the Apollo-SouthBay ColumbiaPark [37] test set.

Original: Note that this data is from an unseen, different environment (collected in the U.S.)with a different type of car setup, comparing to the training data (collected in Germany).Our model Small Large No Skip
Revised: Note that this data is from an unseen, different environment (collected in the U.S.)with a different type of car setup, comparing to the training data (collected in Germany).

Original: No Circular

Original: [Table]

Original: Loss

Original: [Table]

Original: From Start

Original: [Table]

Original: trained

Original: [Table] 4M Parameters [Table] achieves a mean Chamfer distance of 0 .
Revised: Our modelachieves a mean Chamfer distance of 0 .

Original: 934 m 2 on the Apollo test set.
Revised: 934 m 2 on the Apollo test set.

Original: The performance gap compared to the results in Tab. 1 is due the fact that the training data has a maximum range of 85 m butthe Apollo test data contains points up to a range of 110 m.
Revised: The performance gap compared to the results in Tab. 1 is due the fact that the training data has a maximum range of 85 m butthe Apollo test data contains points up to a range of 110 m.

Original: Thus, we ﬁne-tune the model on the
Revised: Thus, we ﬁne-tune the model on the

Original: Apollo-SouthBay ColumbiaPark
Revised: Apollo-SouthBay ColumbiaPark

Original: [36] training set for a single epoch.
Revised: [37] training set for a single epoch.

Original: This yields a mean Chamferdistance of 0 .
Revised: This yields a mean Chamferdistance of 0 .

Original: 426 m 2 and demonstrates the generalization capability of our approach and the advantage of self-supervised training.
Revised: 426 m 2 and demonstrates the generalization capability of our approach and the advantage of self-supervised training.

Original: We provide an additional experiment on the effect of using moretraining data in Appendix C. Fig.
Revised: We provide an additional experiment on the effect of using moretraining data in Appendix C. Fig.

Original: 5 shows that the ﬁne-tuned model reduces the Chamfer distance bypredicting more accurate shapes and more distant points.
Revised: 5 shows that the ﬁne-tuned model reduces the Chamfer distance bypredicting more accurate shapes and more distant points.

Original: Note that compared to Fig.
Revised: Note that compared to Fig.

Original: 4, the differentsensor mount causes a changed pattern of invalid points around the car.
Revised: 4, the differentsensor mount causes a changed pattern of invalid points around the car.

Original: Our approach can correctlypredict the re-projection mask.
Revised: Our approach can correctlypredict the re-projection mask.

Original:  Conclusion
Revised:  Conclusion

Original: In this paper, we presented a novel approach to self-supervised point cloud prediction with 3D convolutional neural networks.
Revised: In this paper, we presented a novel approach to self-supervised point cloud prediction with 3D convolutional neural networks.

Original: Our method exploits the range image representation to jointly extractspatio-temporal features from the input point clouds sequence.
Revised: Our method exploits the range image representation to jointly extractspatio-temporal features from the input point clouds sequence.

Original: This allows us to successfully predictdetailed, full-scale, future 3D point clouds during online operation.
Revised: This allows us to successfully predictdetailed, full-scale, future 3D point clouds during online operation.

Original: We implemented and evaluatedour approach on different datasets and provided comparisons to other existing techniques and supported all claims made in this work.
Revised: We implemented and evaluatedour approach on different datasets and provided comparisons to other existing techniques and supported all claims made in this work.

Original: The experiments suggest that our method outperforms existingbaselines and generalizes well to different sensor mounts in unseen environments.
Revised: The experiments suggest that our method outperforms existingbaselines and generalizes well to different sensor mounts in unseen environments.