Dense Point Diffusion for 3D Object Detection
2020 International Conference on 3D Vision (3DV)(2020)
摘要
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 code
1
has been released to facilitate the reproduction of the results.
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关键词
receptive field,multiscale feature fusion,object detection task,state-of-the-art 3D object detectors,high point resolution,point clouds,backbone network,point convolution,dense point diffusion module
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