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[bibtex]@InProceedings{Wang_2022_CVPR, author = {Wang, Jun and Li, Xiaolong and Sullivan, Alan and Abbott, Lynn and Chen, Siheng}, title = {PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {4419-4428} }
PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences
Abstract
We propose a point-based spatiotemporal pyramid architecture, called PointMotionNet, to learn motion information from a sequence of large-scale 3D LiDAR point clouds. A core component of PointMotionNet is a novel technique for point-based spatiotemporal convolution, which finds the point correspondences across time by leveraging a time-invariant spatial neighboring space and extracts spatiotemporal features. To validate PointMotionNet, we consider two motion-related tasks: point-based motion prediction and multisweep semantic segmentation. For each task, we design an end-to-end system where PointMotionNet is the core module that learns motion information. We conduct extensive experiments and show that i) for point-based motion prediction, PointMotionNet achieves less than 0.5m mean squared error on Argoverse dataset, which is a significant improvement over existing methods; and ii) for multisweep semantic segmentation, PointMotionNet with a pretrained segmentation backbone outperforms previous SOTA by over 3.3% mIoU on SemanticKITTI dataset with 25 classes including 6 moving objects.
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