Instance segmentation is a fundamental research in computer vision, especially in autonomous driving. However, manual mask annotation for instance segmentation is quite time-consuming and costly. To address this problem, we propose a novel framework called Multimodal Weakly Supervised Instance Segmentation (MWSIS), which incorporates various fine-grained label generation and correction modules for both 2D and 3D modalities to improve the quality of pseudo labels, along with a new multimodal cross-supervision approach.

Radar sensors are vital for autonomous driving due to their consistent and dependable performance, even in challenging weather conditions. Semantic segmentation of moving objects in sparse radar point clouds is an emerging task that contributes to improving the safety of autonomous driving. In this paper, we propose a scheme to process points of multi-scan data into a single frame with the availability of temporal features. Our novel network, called Spatial and Temporal Awareness Network (STA-Net), enables points at different times to interact and establish their spatiotemporal connections to comprehend the surroundings of these points.