Jigsaw++: Imagining Complete Shape Priors for Object Reassembly
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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.
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50 most cited of 60 All 60 (JSON)
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Citing papers per year: 2024: 1; 2025: 1; 2026: 4.
6 papers.
- 2025GARF: Learning Generalizable 3D Reassembly for Real-World Fracturescited by 13
- 2024Geometric Point Attention Transformer for 3D Shape Reassemblycited by 3
- 2026CRAG: Can 3D Generative Models Help 3D Assembly?cited by 1
- 2026SARe: Structure-Aware Generative 3D Fragment Reassemblycited by 1
- 2026Automatic assembly modeling through attention-based assembly feature mate prediction and assembly feature pattern recognitioncited by 0
- 2026LLM2R: From Large Language Models to 3D Reassemblerscited by 0
- 2026LLM2R: From Large Language Models to 3D Reassemblers13 Oct 2026
- 2026Automatic assembly modeling through attention-based assembly feature mate prediction and assembly feature pattern recognition10 Oct 2026
- 2026SARe: Structure-Aware Generative 3D Fragment Reassembly23 Mar 2026
- 2026CRAG: Can 3D Generative Models Help 3D Assembly?26 Feb 2026
- 2025GARF: Learning Generalizable 3D Reassembly for Real-World Fractures7 Apr 2025
- 2024Geometric Point Attention Transformer for 3D Shape Reassembly26 Nov 2024