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ATPPNet: Attention based Temporal Point cloud Prediction Network

Kaustab Pal, Aditya Sharma, Avinash Sharma, K. Madhava Krishna

TL;DR

ATPPNet addresses the challenging task of predicting future LiDAR point clouds from past sequences by fusing Conv-LSTM-based spatio-temporal modeling with both spatial and channel-wise attention, plus a 3D-CNN branch to capture global context. The architecture yields future range images and reprojection masks that enable high-fidelity point-cloud reconstruction, trained in a self-supervised manner and evaluated on KITTI and nuScenes where it achieves state-of-the-art range loss and Chamfer-distance improvements and real-time inference. Ablation studies confirm the value of attention, temporal modeling depth, and the 3D-CNN component, while downstream odometry experiments demonstrate practical gains in ego-motion estimation. The approach promises improved perception and localization for autonomous navigation tasks, with potential for active localization strategies to exploit regions of low drift.

Abstract

Point cloud prediction is an important yet challenging task in the field of autonomous driving. The goal is to predict future point cloud sequences that maintain object structures while accurately representing their temporal motion. These predicted point clouds help in other subsequent tasks like object trajectory estimation for collision avoidance or estimating locations with the least odometry drift. In this work, we present ATPPNet, a novel architecture that predicts future point cloud sequences given a sequence of previous time step point clouds obtained with LiDAR sensor. ATPPNet leverages Conv-LSTM along with channel-wise and spatial attention dually complemented by a 3D-CNN branch for extracting an enhanced spatio-temporal context to recover high quality fidel predictions of future point clouds. We conduct extensive experiments on publicly available datasets and report impressive performance outperforming the existing methods. We also conduct a thorough ablative study of the proposed architecture and provide an application study that highlights the potential of our model for tasks like odometry estimation.

ATPPNet: Attention based Temporal Point cloud Prediction Network

TL;DR

ATPPNet addresses the challenging task of predicting future LiDAR point clouds from past sequences by fusing Conv-LSTM-based spatio-temporal modeling with both spatial and channel-wise attention, plus a 3D-CNN branch to capture global context. The architecture yields future range images and reprojection masks that enable high-fidelity point-cloud reconstruction, trained in a self-supervised manner and evaluated on KITTI and nuScenes where it achieves state-of-the-art range loss and Chamfer-distance improvements and real-time inference. Ablation studies confirm the value of attention, temporal modeling depth, and the 3D-CNN component, while downstream odometry experiments demonstrate practical gains in ego-motion estimation. The approach promises improved perception and localization for autonomous navigation tasks, with potential for active localization strategies to exploit regions of low drift.

Abstract

Point cloud prediction is an important yet challenging task in the field of autonomous driving. The goal is to predict future point cloud sequences that maintain object structures while accurately representing their temporal motion. These predicted point clouds help in other subsequent tasks like object trajectory estimation for collision avoidance or estimating locations with the least odometry drift. In this work, we present ATPPNet, a novel architecture that predicts future point cloud sequences given a sequence of previous time step point clouds obtained with LiDAR sensor. ATPPNet leverages Conv-LSTM along with channel-wise and spatial attention dually complemented by a 3D-CNN branch for extracting an enhanced spatio-temporal context to recover high quality fidel predictions of future point clouds. We conduct extensive experiments on publicly available datasets and report impressive performance outperforming the existing methods. We also conduct a thorough ablative study of the proposed architecture and provide an application study that highlights the potential of our model for tasks like odometry estimation.
Paper Structure (18 sections, 8 equations, 3 figures, 6 tables)

This paper contains 18 sections, 8 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Our Approach
  3. Overall Architecture
  4. Convolution Encoder & Decoder block
  5. Conv-LSTM encoder
  6. Attention Module
  7. Conv-LSTM decoder
  8. $3D$-CNN block
  9. Loss Function
  10. Experiments and Results
  11. Experimental Settings
  12. KITTI Odometry dataset kitti
  13. nuScenes dataset caesar2020nuscenes
  14. Qualitative Analysis
  15. Quantitative Analysis
  16. ...and 3 more sections

Figures (3)

  • Figure 1: (a) Predicted range images by our ATPPNet and existing methods in comparison to ground truth and, (b) the $3D$ rendering of the predicted point cloud by ATPPNet (blue) and ground-truth (red). Green circle/rectangle highlights regions where ATPPNet's predictions are superior.
  • Figure 2: ATPPNet Architecture. ATPPNet leverages Conv-LSTM along with channel-wise and spatial attention dually complemented by a 3D-CNN branch for extracting an enhanced spatio-temporal context to recover high quality fidel predictions of future point clouds.
  • Figure 3: Qualitative comparison conducted on sequence 10 of the KITTI odometry dataset. The predicted points (blue) and the ground truth points (red) are combined for a better visual comparison. The top row shows the point clouds at prediction step $t+1$ and the bottom row shows the point clouds at prediction step $t+5$. The areas of interest are circled in green.