articleJun 1, 2019Closed access

PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud

Chinese University of Hong Kong

Indexed incrossref

Abstract

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to…

Citation impact

2,952
total citations
FWCI
251.05
Percentile
100%
References
68
Citations per year

Authors

3

Topics & keywords

Keywords
  • Point cloud
  • Computer science
  • Benchmark (surveying)
  • Artificial intelligence
  • Object detection
  • Computer vision
  • Point (geometry)
  • Object (grammar)
UN Sustainable Development Goals
  • Sustainable cities and communities
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