Fully Sparse 3D Panoptic Occupancy Prediction

Occupancy prediction plays a pivotal role in the realm of autonomous driving. Previous methods typically constructs a dense 3D volume, neglecting the inherent sparsity of the scene, which results in a high computational cost. Furthermore, these methods are limited to semantic occupancy and fail to differentiate between distinct instances. To exploit the sparsity property and ensure instance-awareness, we introduce a novel fully sparse panoptic occupancy network, termed SparseOcc. SparseOcc initially reconstructs a sparse 3D representation from visual inputs. Subsequently, it employs sparse instance queries to predict each object instance from the sparse 3D representation. These instance queries interact with 2D features via mask-guided sparse sampling, thereby circumventing the need for costly dense features or global attention. Additionally, we have established the first-ever vision-centric panoptic occupancy benchmark. SparseOcc demonstrates its efficacy on the Occ3D-nus dataset by achieving a mean Intersection over Union (mIoU) of 26.0, while maintaining a real-time inference speed of 25.4 FPS. By incorporating temporal modeling from the preceding 8 frames, SparseOcc further improves its performance, achieving 30.9 mIoU without whistles and bells. Code will be made available.