  LiDAR-based 3D object detection plays an essential role in autonomous
driving. Existing high-performing 3D object detectors usually build dense
feature maps in the backbone network and prediction head. However, the
computational costs introduced by the dense feature maps grow quadratically as
the perception range increases, making these models hard to scale up to
long-range detection. Some recent works have attempted to construct fully
sparse detectors to solve this issue; nevertheless, the resulting models either
rely on a complex multi-stage pipeline or exhibit inferior performance. In this
work, we propose SAFDNet, a straightforward yet highly effective architecture,
tailored for fully sparse 3D object detection. In SAFDNet, an adaptive feature
diffusion strategy is designed to address the center feature missing problem.
We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2
datasets. SAFDNet performed slightly better than the previous SOTA on the first
two datasets but much better on the last dataset, which features long-range
detection, verifying the efficacy of SAFDNet in scenarios where long-range
detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous
best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded
2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x
faster. The code will be available at https://github.com/zhanggang001/HEDNet.
