Computer Science > Computer Vision and Pattern Recognition
arXiv:2301.11497 (cs)
[Submitted on 27 Jan 2023 (v1), last revised 1 Jun 2023 (this version, v2)]
Title:D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
View a PDF of the paper titled D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts, by Fenggen Yu and 4 other authors
View PDFAbstract:We present D$^2$CSG, a neural model composed of two dual and complementary network branches, with dropouts, for unsupervised learning of compact constructive solid geometry (CSG) representations of 3D CAD shapes. Our network is trained to reconstruct a 3D shape by a fixed-order assembly of quadric primitives, with both branches producing a union of primitive intersections or inverses. A key difference between D$^2$CSG and all prior neural CSG models is its dedicated residual branch to assemble the potentially complex shape complement, which is subtracted from an overall shape modeled by the cover branch. With the shape complements, our network is provably general, while the weight dropout further improves compactness of the CSG tree by removing redundant primitives. We demonstrate both quantitatively and qualitatively that D$^2$CSG produces compact CSG reconstructions with superior quality and more natural primitives than all existing alternatives, especially over complex and high-genus CAD shapes.
| Comments: | 9 pages |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) |
| Cite as: | arXiv:2301.11497 [cs.CV] |
| (orarXiv:2301.11497v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2301.11497 arXiv-issued DOI via DataCite |
Submission history
From: Fenggen Yu [view email][v1] Fri, 27 Jan 2023 02:13:14 UTC (27,615 KB)
[v2] Thu, 1 Jun 2023 17:32:24 UTC (36,355 KB)
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View a PDF of the paper titled D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts, by Fenggen Yu and 4 other authors
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