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.

提出了一种基于稀疏查询的 SparseDet 稀疏三维物体检测方法，通过局部多尺度特征聚合模块和全局特征聚合模块，完整地捕捉了上下文信息，提高了代理物体表达能力。实验结果在 nuScenes 和 KITTI 上验证了该方法的有效性。