CUPID: Generative 3D Reconstruction via Joint Object and Pose Modeling
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915635198427136 |
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| author | Huang, Binbin Duan, Haobin Zhao, Yiqun Zhao, Zibo Ma, Yi Gao, Shenghua |
| author_facet | Huang, Binbin Duan, Haobin Zhao, Yiqun Zhao, Zibo Ma, Yi Gao, Shenghua |
| contents | We introduce Cupid, a generative 3D reconstruction framework that jointly models the full distribution over both canonical objects and camera poses. Our two-stage flow-based model first generates a coarse 3D structure and 2D-3D correspondences to estimate the camera pose robustly. Conditioned on this pose, a refinement stage injects pixel-aligned image features directly into the generative process, marrying the rich prior of a generative model with the geometric fidelity of reconstruction. This strategy achieves exceptional faithfulness, outperforming state-of-the-art reconstruction methods by over 3 dB PSNR and 10% in Chamfer Distance. As a unified generative model that decouples the object and camera pose, Cupid naturally extends to multi-view and scene-level reconstruction tasks without requiring post-hoc optimization or fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CUPID: Generative 3D Reconstruction via Joint Object and Pose Modeling Huang, Binbin Duan, Haobin Zhao, Yiqun Zhao, Zibo Ma, Yi Gao, Shenghua Computer Vision and Pattern Recognition We introduce Cupid, a generative 3D reconstruction framework that jointly models the full distribution over both canonical objects and camera poses. Our two-stage flow-based model first generates a coarse 3D structure and 2D-3D correspondences to estimate the camera pose robustly. Conditioned on this pose, a refinement stage injects pixel-aligned image features directly into the generative process, marrying the rich prior of a generative model with the geometric fidelity of reconstruction. This strategy achieves exceptional faithfulness, outperforming state-of-the-art reconstruction methods by over 3 dB PSNR and 10% in Chamfer Distance. As a unified generative model that decouples the object and camera pose, Cupid naturally extends to multi-view and scene-level reconstruction tasks without requiring post-hoc optimization or fine-tuning. |
| title | CUPID: Generative 3D Reconstruction via Joint Object and Pose Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.20776 |