Fully Sparse 3D Occupancy Prediction
arxiv(2023)
摘要
Occupancy prediction plays a pivotal role in autonomous driving. Previous
methods typically construct dense 3D volumes, neglecting the inherent sparsity
of the scene and suffering high computational costs. To bridge the gap, we
introduce a novel fully sparse occupancy network, termed SparseOcc. SparseOcc
initially reconstructs a sparse 3D representation from visual inputs and
subsequently predicts semantic/instance occupancy from the 3D sparse
representation by sparse queries. A mask-guided sparse sampling is designed to
enable sparse queries to interact with 2D features in a fully sparse manner,
thereby circumventing costly dense features or global attention. Additionally,
we design a thoughtful ray-based evaluation metric, namely RayIoU, to solve the
inconsistency penalty along depths raised in traditional voxel-level mIoU
criteria. SparseOcc demonstrates its effectiveness by achieving a RayIoU of
34.0, while maintaining a real-time inference speed of 17.3 FPS, with 7 history
frames inputs. By incorporating more preceding frames to 15, SparseOcc
continuously improves its performance to 35.1 RayIoU without whistles and
bells. Code is available at https://github.com/MCG-NJU/SparseOcc.
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