  As the perception range of LiDAR expands, LiDAR-based 3D object detection
contributes ever-increasingly to the long-range perception in autonomous
driving. Mainstream 3D object detectors often build dense feature maps, where
the cost is quadratic to the perception range, making them hardly scale up to
the long-range settings. To enable efficient long-range detection, we first
propose a fully sparse object detector termed FSD. FSD is built upon the
general sparse voxel encoder and a novel sparse instance recognition (SIR)
module. SIR groups the points into instances and applies highly-efficient
instance-wise feature extraction. The instance-wise grouping sidesteps the
issue of the center feature missing, which hinders the design of the fully
sparse architecture. To further enjoy the benefit of fully sparse
characteristic, we leverage temporal information to remove data redundancy and
propose a super sparse detector named FSD++. FSD++ first generates residual
points, which indicate the point changes between consecutive frames. The
residual points, along with a few previous foreground points, form the super
sparse input data, greatly reducing data redundancy and computational overhead.
We comprehensively analyze our method on the large-scale Waymo Open Dataset,
and state-of-the-art performance is reported. To showcase the superiority of
our method in long-range detection, we also conduct experiments on Argoverse 2
Dataset, where the perception range ($200m$) is much larger than Waymo Open
Dataset ($75m$). Code is open-sourced at https://github.com/tusen-ai/SST.
