SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection
<|reference_start|>SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection: LiDAR-based sparse 3D object detection plays a crucial role in autonomous driving applications due to its computational efficiency advantages. Existing methods either use the features of a single central voxel as an object proxy, or treat an aggregated cluster of foreground points as an object proxy. However, the former lacks the ability to aggregate contextual information, resulting in insufficient information expression in object proxies. The latter relies on multi-stage pipelines and auxiliary tasks, which reduce the inference speed. To maintain the efficiency of the sparse framework while fully aggregating contextual information, in this work, we propose SparseDet which designs sparse queries as object proxies. It introduces two key modules, the Local Multi-scale Feature Aggregation (LMFA) module and the Global Feature Aggregation (GFA) module, aiming to fully capture the contextual information, thereby enhancing the ability of the proxies to represent objects. Where LMFA sub-module achieves feature fusion across different scales for sparse key voxels %which does this through via coordinate transformations and using nearest neighbor relationships to capture object-level details and local contextual information, GFA sub-module uses self-attention mechanisms to selectively aggregate the features of the key voxels across the entire scene for capturing scene-level contextual information. Experiments on nuScenes and KITTI demonstrate the effectiveness of our method. Specifically, on nuScene, SparseDet surpasses the previous best sparse detector VoxelNeXt by 2.2\% mAP with 13.5 FPS, and on KITTI, it surpasses VoxelNeXt by 1.12\% $\mathbf{AP_{3D}}$ on hard level tasks with 17.9 FPS.<|reference_end|>
@article{liu2024sparsedet:,
    title={SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based
  3D Object Detection},
    author={Lin Liu, Ziying Song, Qiming Xia, Feiyang Jia, Caiyan Jia, Lei Yang,
  Hongyu Pan},
    journal={arXiv preprint arXiv:2406.10907},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10907},
    primaryClass={cs.CV}
}