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Poster
Unsupervised Learning of Shape Programs with Repeatable Implicit Parts
Boyang Deng · Sumith Kulal · Zhengyang Dong · Congyue Deng · Yonglong Tian · Jiajun Wu

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #201
in Poster Session 2 »

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 Repeatable Implicit Parts (ProGRIP). Using implicit functions to represent parts, ProGRIP greatly boosts the representation capacity of shape programs while preserving the higher-level structure of repetitions and symmetry. Meanwhile, we free ProGRIP from any inaccessible supervised training via devising a matching-based unsupervised training objective. Our empirical studies show that ProGRIP outperforms existing structured representations in both shape reconstruction fidelity and segmentation accuracy of semantic parts.

Keywords: unsupervised shape decomposition · shape programs · structured shape representation
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Boyang Deng (Stanford University)
Sumith Kulal (Stanford University)
Zhengyang Dong (Stanford University)
Congyue Deng (Stanford University)
Yonglong Tian (Massachusetts Institute of Technology)
Jiajun Wu (Stanford University)

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