MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding. (arXiv:2107.00346v2 [cs.CV] UPDATED)

At the heart of all automated driving systems is the ability to sense the
surroundings, e.g., through semantic segmentation of LiDAR sequences, which
experienced a remarkable progress due to the release of large datasets such as
SemanticKITTI and nuScenes-LidarSeg. While most previous works focus on sparse
segmentation of the LiDAR input, dense output masks provide self-driving cars
with almost complete environment information. In this paper, we introduce MASS
- a Multi-Attentional Semantic Segmentation model specifically built for dense
top-view understanding …

arxiv cv data lidar segmentation semantic top