mlsanityData to 14 Oct 2026

Jigsaw++: Imagining Complete Shape Priors for Object Reassembly

arXiv 2410.11816 ↗First posted 15 Oct 2024cs.CVcited by 6

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Abstract (arXiv)

The automatic assembly problem has attracted increasing interest due to its complex challenges that involve 3D representation. This paper introduces Jigsaw++, a novel generative method designed to tackle the multifaceted challenges of reconstructing complete shape for the reassembly problem. Existing approach focusing primarily on piecewise information for both part and fracture assembly, often overlooking the integration of complete object prior. Jigsaw++ distinguishes itself by learning a shape prior of complete objects. It employs the proposed "retargeting" strategy that effectively leverages the output of any existing assembly method to generate complete shape reconstructions. This capability allows it to function orthogonally to the current methods. Through extensive evaluations on Breaking Bad dataset and PartNet, Jigsaw++ has demonstrated its effectiveness, reducing reconstruction errors and enhancing the precision of shape reconstruction, which sets a new direction for future reassembly model developments.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 60 of its 78 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

60 of 78 references.

References · 60

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38 more
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50 most cited of 60 All 60 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.