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Dense Point Diffusion for 3D Object Detection
- Source :
- 3DV
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- 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.1https://github.com/AsahiLiu/PointDetectron
- Subjects :
- Backbone network
Source code
business.industry
Computer science
media_common.quotation_subject
010401 analytical chemistry
Point cloud
010501 environmental sciences
Object (computer science)
01 natural sciences
Object detection
0104 chemical sciences
Convolution
Point (geometry)
Computer vision
Artificial intelligence
business
0105 earth and related environmental sciences
media_common
Block (data storage)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 International Conference on 3D Vision (3DV)
- Accession number :
- edsair.doi...........6f3b618c5c5c0b93882aaec944ec8236
- Full Text :
- https://doi.org/10.1109/3dv50981.2020.00086