Learning Generative Models of Shape Handles
Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomír Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji
2020年份
15顶会引用
摘要
Gallery of 3D shapes generated as sets of handles (zoom for details). We propose a class of generative models for synthesizing sets of handles -lightweight proxies that can be easily utilized for high-level tasks such as shape editing, parsing, animation etc. Our model can generate sets with different cardinality and is flexible to work with various types of handles, such as sphere-mesh handles [37] (first and third figures) and cuboids (middle figure).
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引用它的顶会 Paper15
- Approximate convex decomposition for 3D meshes with collision-aware concavity and tree searchXinyue Wei, Minghua Liu, Zhan Ling, Hao SuSIGGRAPH 2022 · 被引用 79 次
- JoinABLe: Learning Bottom-up Assembly of Parametric CAD JointsKarl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian 等CVPR 2022 · 被引用 56 次
- Unsupervised learning for cuboid shape abstraction via joint segmentation from point cloudsKaizhi Yang, Xuejin ChenSIGGRAPH 2021 · 被引用 52 次
- CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point CloudsEric-Tuan Lê, Minhyuk Sung, Duygu Ceylan, Radomír Mech 等ICCV 2021 · 被引用 49 次
- DAG amendment for inverse control of parametric shapesÉlie Michel, Tamy BoubekeurSIGGRAPH 2021 · 被引用 29 次
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