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.

翻译：点云预测是自动驾驶领域中一项重要但具有挑战性的任务。其目标是预测未来点云序列，在保持物体结构的同时精确表征其时序运动。这些预测的点云有助于执行其他后续任务，例如用于碰撞规避的物体轨迹估计，或利用最小里程计漂移估算位置。本文提出ATPPNet——一种新颖的网络架构，该架构能够基于激光雷达传感器获取的历史时序点云序列预测未来点云序列。ATPPNet结合Conv-LSTM与通道级和空间注意力机制，并通过3D-CNN分支进行双重互补，以提取增强的时空上下文信息，从而恢复高质量的未来点云保真预测。我们在公开数据集上开展了大量实验，其性能显著超越现有方法。同时，我们对该架构进行了详尽的消融研究，并提供了应用案例研究，突显了该模型在里程计估计等任务中的应用潜力。