Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are unreliable as SfM algori...
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| Veröffentlicht in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 13 - 23 |
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| Sprache: | Englisch |
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01.06.2023
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| ISSN: | 1063-6919 |
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| Abstract | Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are unreliable as SfM algorithms often fail or produce erroneous poses on challenging videos with highly dynamic objects, poorly textured surfaces, and rotating camera motion. We address this robustness issue by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). We demonstrate the robustness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods. |
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| AbstractList | Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are unreliable as SfM algorithms often fail or produce erroneous poses on challenging videos with highly dynamic objects, poorly textured surfaces, and rotating camera motion. We address this robustness issue by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). We demonstrate the robustness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods. |
| Author | Saraf, Ayush Meuleman, Andreas Kopf, Johannes Tseng, Hung-Yu Chuang, Yung-Yu Huang, Jia-Bin Liu, Yu-Lun Gao, Chen Kim, Changil |
| Author_xml | – sequence: 1 givenname: Yu-Lun surname: Liu fullname: Liu, Yu-Lun organization: National Taiwan University – sequence: 2 givenname: Chen surname: Gao fullname: Gao, Chen organization: Meta – sequence: 3 givenname: Andreas surname: Meuleman fullname: Meuleman, Andreas organization: KAIST – sequence: 4 givenname: Hung-Yu surname: Tseng fullname: Tseng, Hung-Yu organization: Meta – sequence: 5 givenname: Ayush surname: Saraf fullname: Saraf, Ayush organization: Meta – sequence: 6 givenname: Changil surname: Kim fullname: Kim, Changil organization: Meta – sequence: 7 givenname: Yung-Yu surname: Chuang fullname: Chuang, Yung-Yu organization: National Taiwan University – sequence: 8 givenname: Johannes surname: Kopf fullname: Kopf, Johannes organization: Meta – sequence: 9 givenname: Jia-Bin surname: Huang fullname: Huang, Jia-Bin organization: Meta |
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| Snippet | Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that... |
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| SubjectTerms | 3D from multi-view and sensors Cameras Computer vision Dynamics Heuristic algorithms Reconstruction algorithms Robustness Structure from motion |
| Title | Robust Dynamic Radiance Fields |
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