Recent works of point clouds show that mulit-frame spatio-temporal modeling
outperforms single-frame versions by utilizing cross-frame information. In this
paper, we further improve spatio-temporal point cloud feature learning with a
flexible module called ASAP considering both attention and structure
information across frames, which we find as two important factors for
successful segmentation in dynamic point clouds. Firstly, our ASAP module
contains a novel attentive temporal embedding layer to fuse the relatively
informative local features across frames in a recurrent fashion. Secondly, an
efficient spatio-temporal correlation method is proposed to exploit more local
structure for embedding, meanwhile enforcing temporal consistency and reducing
computation complexity. Finally, we show the generalization ability of the
proposed ASAP module with different backbone networks for point cloud sequence
segmentation. Our ASAP-Net (backbone plus ASAP module) outperforms baselines
and previous methods on both Synthia and SemanticKITTI datasets (+3.4 to +15.2
mIoU points with different backbones). Code is availabe at
this https URL

此篇研究论文提出一种灵活的模块 ASAP 进行点云的时空特征学习，在考虑多帧跨帧信息的同时结合注意力和结构信息，提高了点云序列的分割准确性和泛化能力，并在 Synthia 和 SemanticKITTI 数据集上表现出比基线和以往方法更好的效果。