  In this paper, we propose an efficient feature pruning strategy for 3D small
object detection. Conventional 3D object detection methods struggle on small
objects due to the weak geometric information from a small number of points.
Although increasing the spatial resolution of feature representations can
improve the detection performance on small objects, the additional
computational overhead is unaffordable. With in-depth study, we observe the
growth of computation mainly comes from the upsampling operation in the decoder
of 3D detector. Motivated by this, we present a multi-level 3D detector named
DSPDet3D which benefits from high spatial resolution to achieves high accuracy
on small object detection, while reducing redundant computation by only
focusing on small object areas. Specifically, we theoretically derive a dynamic
spatial pruning (DSP) strategy to prune the redundant spatial representation of
3D scene in a cascade manner according to the distribution of objects. Then we
design DSP module following this strategy and construct DSPDet3D with this
efficient module. On ScanNet and TO-SCENE dataset, our method achieves leading
performance on small object detection. Moreover, DSPDet3D trained with only
ScanNet rooms can generalize well to scenes in larger scale. It takes less than
2s to directly process a whole building consisting of more than 4500k points
while detecting out almost all objects, ranging from cups to beds, on a single
RTX 3090 GPU. Project page: https://xuxw98.github.io/DSPDet3D/.
