CSG-Stump: a learning friendly CSG-like representation for interpretable shape parsing

Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Haiyong Jiang, Zhongang Cai, Junzhe Zhang, Liang Pan, Mingyuan Zhang, Haiyu Zhao, Shuai Yi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)

Abstract

Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem. This paper presents CSG-Stump Net, an unsupervised end-to-end network for learning shapes from point clouds and discovering the underlying constituent modeling primitives and operations as well. At the core is a three-level structure called CSG-Stump, consisting of a complement layer at the bottom, an intersection layer in the middle, and a union layer at the top. CSG-Stump is proven to be equivalent to CSG in terms of representation, therefore inheriting the interpretable, compact and editable nature of CSG while freeing from CSG's complex tree structures. Particularly, the CSG-Stump has a simple and regular structure, allowing neural networks to give outputs of a constant dimensionality, which makes itself deep-learning friendly. Due to these characteristics of CSG-Stump, CSGStump Net achieves superior results compared to previous CSG-based methods and generates much more appealing shapes, as confirmed by extensive experiments.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
EditorsDima Damen, Tal Hassner, Chris Pal, Yoichi Sato
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages12458-12467
Number of pages10
ISBN (Electronic)9781665428125
ISBN (Print)9781665428132
DOIs
Publication statusPublished - 2021
EventIEEE International Conference on Computer Vision 2021 - Online, United States of America
Duration: 11 Oct 202117 Oct 2021
https://iccv2021.thecvf.com/home (Website)
https://ieeexplore.ieee.org/xpl/conhome/9709627/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

ConferenceIEEE International Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Country/TerritoryUnited States of America
CityOnline
Period11/10/2117/10/21
Internet address

Cite this