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SCIENCE CHINA Information Sciences, Volume 63 , Issue 12 : 224101(2020) https://doi.org/10.1007/s11432-018-9689-9

Semantic part segmentation of single-view point cloud

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  • ReceivedSep 15, 2018
  • AcceptedNov 30, 2018
  • PublishedSep 28, 2020

Abstract

Single-view point cloud is the most commonly type of raw 3D data. Previous studies have focused on the object classification problem, and a few studies have been concerned with segmenting and labeling the semantic parts of single-view point cloud. In this paper, we propose a new method to solve the point cloud semantic part segmentation problem via annotation transference. First, we established a database of 3D synthetic CAD models. Taking a single-view point cloud as input, we retrieved the matching models from the database. Using the point-level correspondences, we transferred the annotations onto the input. We performed experiments on two public benchmarks and one raw scanned dataset. Compared to five other state-of-the-art methods, our method achieves a comparable accuracy with a low cost.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61502023, 61532003).


Supplement

Videos and other supplemental documents.


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