NoPe‐NeRF++: Local‐to‐Global Optimization of NeRF with No Pose Prior
In this paper, we introduce NoPe‐NeRF++, a novel local‐to‐global optimization algorithm for training Neural Radiance Fields (NeRF) without requiring pose priors. Existing methods, particularly NoPe‐NeRF, which focus solely on the local relationships within images, often struggle to recover accurate...
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| Published in: | Computer graphics forum Vol. 44; no. 2 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Blackwell Publishing Ltd
01.05.2025
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | In this paper, we introduce NoPe‐NeRF++, a novel local‐to‐global optimization algorithm for training Neural Radiance Fields (NeRF) without requiring pose priors. Existing methods, particularly NoPe‐NeRF, which focus solely on the local relationships within images, often struggle to recover accurate camera poses in complex scenarios. To overcome the challenges, our approach begins with a relative pose initialization with explicit feature matching, followed by a local joint optimization to enhance the pose estimation for training a more robust NeRF representation. This method significantly improves the quality of initial poses. Additionally, we introduce global optimization phase that incorporates geometric consistency constraints through bundle adjustment, which integrates feature trajectories to further refine poses and collectively boost the quality of NeRF. Notably, our method is the first work that seamlessly combines the local and global cues with NeRF, and outperforms state‐of‐the‐art methods in both pose estimation accuracy and novel view synthesis. Extensive evaluations on benchmark datasets demonstrate our superior performance and robustness, even in challenging scenes, thus validating our design choices. |
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| AbstractList | In this paper, we introduce NoPe‐NeRF++, a novel local‐to‐global optimization algorithm for training Neural Radiance Fields (NeRF) without requiring pose priors. Existing methods, particularly NoPe‐NeRF, which focus solely on the local relationships within images, often struggle to recover accurate camera poses in complex scenarios. To overcome the challenges, our approach begins with a relative pose initialization with explicit feature matching, followed by a local joint optimization to enhance the pose estimation for training a more robust NeRF representation. This method significantly improves the quality of initial poses. Additionally, we introduce global optimization phase that incorporates geometric consistency constraints through bundle adjustment, which integrates feature trajectories to further refine poses and collectively boost the quality of NeRF. Notably, our method is the first work that seamlessly combines the local and global cues with NeRF, and outperforms state‐of‐the‐art methods in both pose estimation accuracy and novel view synthesis. Extensive evaluations on benchmark datasets demonstrate our superior performance and robustness, even in challenging scenes, thus validating our design choices. |
| Author | Fan, Lubin Liu, Ligang Guo, Jinhui Shi, Dongbo Ye, Jieping Cao, Shen Wu, Bojian Chen, Renjie |
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| Cites_doi | 10.1109/CVPR52733.2024.01857 10.1109/CVPR52688.2022.01781 10.1145/258734.258854 10.1145/166117.166153 10.1145/3503250 10.1145/1186822.1073232 10.1109/ICCV48922.2021.00580 10.1109/ICCV48922.2021.01072 10.1109/TIP.2003.819861 10.1145/3550469.3555413 10.1109/CVPR52688.2022.00807 10.1145/237170.237196 10.1145/3478513.3480496 10.1109/ICCV48922.2021.01196 10.1109/IROS51168.2021.9636708 10.1109/ICCV.2009.5459148 10.1109/CVPRW.2018.00060 10.1145/237170.237200 10.1109/TPAMI.2007.1049 10.1109/CVPR52729.2023.00799 10.1109/34.88573 10.1109/CVPR52733.2024.01965 10.1109/CVPR52688.2022.00381 10.1145/3618321 10.1109/CVPR.2018.00068 10.1109/ICCV48922.2021.00569 10.1109/CVPR52688.2022.00536 10.1109/ICRA57147.2024.10611269 10.1109/CVPR42600.2020.00499 |
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| Snippet | In this paper, we introduce NoPe‐NeRF++, a novel local‐to‐global optimization algorithm for training Neural Radiance Fields (NeRF) without requiring pose... |
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| SubjectTerms | Bundle adjustment CCS Concepts Computing methodologies → Image‐based rendering Global optimization Optimization Pose estimation |
| Title | NoPe‐NeRF++: Local‐to‐Global Optimization of NeRF with No Pose Prior |
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