  3D scenes are dominated by a large number of background points, which is
redundant for the detection task that mainly needs to focus on foreground
objects. In this paper, we analyze major components of existing sparse 3D CNNs
and find that 3D CNNs ignore the redundancy of data and further amplify it in
the down-sampling process, which brings a huge amount of extra and unnecessary
computational overhead. Inspired by this, we propose a new convolution operator
named spatial pruned sparse convolution (SPS-Conv), which includes two
variants, spatial pruned submanifold sparse convolution (SPSS-Conv) and spatial
pruned regular sparse convolution (SPRS-Conv), both of which are based on the
idea of dynamically determining crucial areas for redundancy reduction. We
validate that the magnitude can serve as important cues to determine crucial
areas which get rid of the extra computations of learning-based methods. The
proposed modules can easily be incorporated into existing sparse 3D CNNs
without extra architectural modifications. Extensive experiments on the KITTI,
Waymo and nuScenes datasets demonstrate that our method can achieve more than
50% reduction in GFLOPs without compromising the performance.
