Exploiting past 3D LiDAR scans to predict future point clouds is a promising
method for autonomous mobile systems to realize foresighted state estimation,
collision avoidance, and planning. In this paper, we address the problem of
predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
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. We propose an end-to-end approach that exploits a 2D range
image representation of each 3D LiDAR scan and concatenates a sequence of range
images to obtain a 3D tensor. Based on such tensors, we develop an
encoder-decoder architecture using 3D convolutions to jointly aggregate spatial
and temporal information of the scene and to predict the future 3D point
clouds. We evaluate our method on multiple datasets and the experimental
results suggest that our method outperforms existing point cloud prediction
architectures and generalizes well to new, unseen environments without
additional fine-tuning. Our method operates online and is faster than the
common LiDAR frame rate of 10 Hz.

本文提出了一种使用 3D LiDAR 扫描来预测未来点云的方法，该方法可用于实现自主移动系统的预测状态估计、避碰和规划，通过使用 2D 范围图像表示扫描数据并连接一系列范围图像来实现端到端的预测，最终通过 3D 卷积来预测未来的 3D 点云，并通过多个数据集进行实验验证，结果表明本文提出的方法比现有的点云预测结构更优且能够很好地适用于新的未知环境。

使用 3D 时空卷积网络进行自监督点云预测

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal  Convolutional Networks

We introduce a new approach for multiscale 3Dsemantic scene completion from
voxelized sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet
backbone with comprehensive multiscale skip connections to enhance feature
flow, along with 3D segmentation heads. On the SemanticKITTI benchmark, our
method performs on par on semantic completion and better on occupancy
completion than all other published methods -- while being significantly
lighter and faster. As such it provides a great performance/speed trade-off for
mobile-robotics applications. The ablation studies demonstrate our method is
robust to lower density inputs, and that it enables very high speed semantic
completion at the coarsest level. Our code is available at
this https URL

本文提出了一种新的方法，利用 2D UNet 骨干网络和全面的多尺度跳跃连接强化特征流，并配备 3D 分割头，在语义完成和占用完成方面性能表现优异，且代码可用。