Jigsaw++: Imagining Complete Shape Priors for Object Reassembly
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911210157375488 |
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| author | Lu, Jiaxin Hua, Gang Huang, Qixing |
| author_facet | Lu, Jiaxin Hua, Gang Huang, Qixing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Jigsaw++: Imagining Complete Shape Priors for Object Reassembly Lu, Jiaxin Hua, Gang Huang, Qixing Computer Vision and Pattern Recognition 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. |
| title | Jigsaw++: Imagining Complete Shape Priors for Object Reassembly |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.11816 |