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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
