Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916279145725952 |
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| author | Weng, Yijia Wen, Bowen Tremblay, Jonathan Blukis, Valts Fox, Dieter Guibas, Leonidas Birchfield, Stan |
| author_facet | Weng, Yijia Wen, Bowen Tremblay, Jonathan Blukis, Valts Fox, Dieter Guibas, Leonidas Birchfield, Stan |
| contents | We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into two stages, each addressing distinct aspects. Our method first reconstructs object-level shape at each state, then recovers the underlying articulation model including part segmentation and joint articulations that associate the two states. By explicitly modeling point-level correspondences and exploiting cues from images, 3D reconstructions, and kinematics, our method yields more accurate and stable results compared to prior work. It also handles more than one movable part and does not rely on any object shape or structure priors. Project page: https://github.com/NVlabs/DigitalTwinArt |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects Weng, Yijia Wen, Bowen Tremblay, Jonathan Blukis, Valts Fox, Dieter Guibas, Leonidas Birchfield, Stan Computer Vision and Pattern Recognition Artificial Intelligence Graphics Robotics We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into two stages, each addressing distinct aspects. Our method first reconstructs object-level shape at each state, then recovers the underlying articulation model including part segmentation and joint articulations that associate the two states. By explicitly modeling point-level correspondences and exploiting cues from images, 3D reconstructions, and kinematics, our method yields more accurate and stable results compared to prior work. It also handles more than one movable part and does not rely on any object shape or structure priors. Project page: https://github.com/NVlabs/DigitalTwinArt |
| title | Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Robotics |
| url | https://arxiv.org/abs/2404.01440 |