3D semantic segmentation on multi-scan large-scale point clouds plays an
important role in autonomous systems. Unlike the single-scan-based semantic
segmentation task, this task requires distinguishing the motion states of
points in addition to their semantic categories. However, methods designed for
single-scan-based segmentation tasks perform poorly on the multi-scan task due
to the lacking of an effective way to integrate temporal information. We
propose MarS3D, a plug-and-play motion-aware module for semantic segmentation
on multi-scan 3D point clouds. This module can be flexibly combined with
single-scan models to allow them to have multi-scan perception abilities. The
model encompasses two key designs: the Cross-Frame Feature Embedding module for
enriching representation learning and the Motion-Aware Feature Learning module
for enhancing motion awareness. Extensive experiments show that MarS3D can
improve the performance of the baseline model by a large margin. The code is
available at this https URL

提出了 MarS3D，一个用于多扫描 3D 点云的语义分割的即插即用的运动感知模块，该模块与单扫描模型灵活结合以具备多扫描感知能力，并通过丰富表示学习和增强运动感知来改善基线模型的性能。