Dense Point Diffusion for 3D Object Detection
Xu Liu, Jiayan Cao, Qianqian Bi, Jian Wang, Boxin Shi, Yichen Wei · 2020
The backbone network adopted in state-of-the-art 3D object detectors lacks a good balance between high point resolution and large receptive field, both of which are desirable for object detection on point clouds. This work proposes Dense Point Diffusion module, a novel backbone network that solves these issues. It adopts dilated point convolution as a building block to enlarge the receptive field and retain the point resolution at the same time. Further, a number of such layers are densely connected, giving rise to large receptive field and multi-scale feature fusion, which are effective for object detection task. Comprehensive experiments verify the efficacy of our approach. The source code1has been released to facilitate the reproduction of the results.