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2026-02-24 08:50:38
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ProGRIP Unsupervised Learning of Shape Programs with Repeatable Implicit Parts NeurIPS 2022 Boyang Deng ★ ♦ Sumith Kulal ★ ♦ Zhengyang Dong ♦ Congyue Deng ♦ Yonglong Tian § Jiajun Wu ♦ ♦ Stanford University § MIT ★ Equal contributions Paper | Talk Abstract: Shape programs encode shape structures by representing object parts as subroutines and constructing the overall shape by composing these subroutines. This usually involves the reuse of subroutines for repeatable parts, enabling the modeling of correlations among shape elements such as geometric similarity. However, existing learning-based shape programs suffer from limited representation capacity because they use coarse geometry representations such as geometric primitives and low-resolution voxel grids. Further, their training requires manually annotated ground-truth programs, which are expensive to attain. We address these limitations by proposing Shape Programs with Repeat...
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