PUA-MOS: End-to-End Point-wise Uncertainty Weighted Aggregation for Moving Object Segmentation

Abstract

Segmenting moving objects in the 3D LiDAR point cloud can provide important guidance to localization, mapping and decision-making for self-driving vehicles. As for the conventional approaches to point cloud segmentation, they rely on semantic-level information, which makes it inevitable for long-tail problems to arise as there are always unseen types of objects on the road. To achieve moving segmentation while avoiding the reliance on the object category, the point motion is identified in this paper by fully exploring and aggregating the point-level geometric consistency in sequential point clouds. More specifically, an end-to-end point-wise uncertainty weighted aggregation approach known as PUA-MOS is proposed to segment the moving points in 3D LiDAR Data. Our method is applicable to estimate point-wise moving mask, scene flow and rigid-body transformation simultaneously in a coarse- to-fine network, where the relations between each prediction are implicitly learned. To explicitly model the inner and inter relations across these predictions among all points, the point- wise estimation and the average value of the same motion points are aggregated according to a predicted uncertainty. Then, the aggregated estimation is fed again into the next-level fusion, where the points will be re-segmented using the aggregated mask from the last level. Through iterative joint aggregation, our PUA-MOS outperforms the previous methods significantly on both KITTI [4] and Waymo [26] datasets. The code will be provided to generate the moving segmentation labels on both datasets for reproduction.