Real-time 3D semantic occupancy prediction for autonomous vehicles using memory-efficient sparse convolution

arXiv:2403.08748v1 Announce Type: cross
Abstract: In autonomous vehicles, understanding the surrounding 3D environment of the ego vehicle in real-time is essential. A compact way to represent scenes while encoding geometric distances and semantic object information is via 3D semantic occupancy maps. State of the art 3D mapping methods leverage transformers with cross-attention mechanisms to elevate 2D vision-centric camera features into the 3D domain. However, these methods encounter significant challenges in real-time applications due to their high computational demands during inference. …

3d mapping abstract art arxiv autonomous autonomous vehicles convolution cs.cv cs.ro encoding environment information mapping maps memory object prediction real-time semantic state state of the art type understanding vehicles via