Pose-free 3D Gaussian splatting via shape-ray estimation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914104712626176 |
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| author | Na, Youngju Kim, Taeyeon Lee, Jumin Han, Kyu Beom Kim, Woo Jae Yoon, Sung-eui |
| author_facet | Na, Youngju Kim, Taeyeon Lee, Jumin Han, Kyu Beom Kim, Woo Jae Yoon, Sung-eui |
| contents | While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-world scenarios, obtaining accurate poses is challenging, leading to noisy pose estimates and geometric misalignments. To address this, we introduce SHARE, a pose-free, feed-forward Gaussian splatting framework that overcomes these ambiguities by joint shape and camera rays estimation. Instead of relying on explicit 3D transformations, SHARE builds a pose-aware canonical volume representation that seamlessly integrates multi-view information, reducing misalignment caused by inaccurate pose estimates. Additionally, anchor-aligned Gaussian prediction enhances scene reconstruction by refining local geometry around coarse anchors, allowing for more precise Gaussian placement. Extensive experiments on diverse real-world datasets show that our method achieves robust performance in pose-free generalizable Gaussian splatting. Code is avilable at https://github.com/youngju-na/SHARE |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22978 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pose-free 3D Gaussian splatting via shape-ray estimation Na, Youngju Kim, Taeyeon Lee, Jumin Han, Kyu Beom Kim, Woo Jae Yoon, Sung-eui Computer Vision and Pattern Recognition While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-world scenarios, obtaining accurate poses is challenging, leading to noisy pose estimates and geometric misalignments. To address this, we introduce SHARE, a pose-free, feed-forward Gaussian splatting framework that overcomes these ambiguities by joint shape and camera rays estimation. Instead of relying on explicit 3D transformations, SHARE builds a pose-aware canonical volume representation that seamlessly integrates multi-view information, reducing misalignment caused by inaccurate pose estimates. Additionally, anchor-aligned Gaussian prediction enhances scene reconstruction by refining local geometry around coarse anchors, allowing for more precise Gaussian placement. Extensive experiments on diverse real-world datasets show that our method achieves robust performance in pose-free generalizable Gaussian splatting. Code is avilable at https://github.com/youngju-na/SHARE |
| title | Pose-free 3D Gaussian splatting via shape-ray estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.22978 |