Accurate 3D Object Detection in Super Sparse Space via Operator-level Sparse Attention

In autonomous driving perception systems, the extreme sparsity of LiDAR point clouds poses a fundamental challenge to achieving high-accuracy 3D object detection under constrained computational budgets. Prevailing paradigms adopt a "sparse to dense" pipeline, where the dense projection introduces significant computing and memory bottlenecks. Emerging fully sparse architectures eliminate this dense projection but suffer from severe inefficiencies in both computation and memory, stemming from indiscriminate processing of all voxels and the incompatibility of standard self-attention with irregular sparse sequences. Furthermore, conventional detection heads rely on fixed-range max pooling for peak localization, which fails under the fully sparse paradigm and degrades detection performance. To address these limitations, we propose an accurate fully sparse 3D detector. Specifically, a Sparse Transformer module based on Sparse Matrix Multiplication is designed to perform attention directly over non-empty voxels and their local window neighborhoods, completely eliminating redundant computation and memory overhead at empty locations. Furthermore, a Progressive Pruning Network is introduced to hierarchically prune low-confidence background voxels during feature extraction, progressively transforming the sparse voxel set into super sparse space that retains only foreground-enriched tokens, thereby enabling stepwise computational reduction as the network deepens. To tackle the peak localization issue, we present a Dynamic Transfusion Head that replaces fixed-range max pooling with learnable bounding-box-aware dynamic pooling. Evaluated on the nuScenes dataset, our method achieves new state-of-the-art accuracy and delivers an improved accuracy-efficiency trade-off, while requiring only half the training epochs of classic methods.