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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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 13 - 23
Hlavní autoři: Liu, Yu-Lun, Gao, Chen, Meuleman, Andreas, Tseng, Hung-Yu, Saraf, Ayush, Kim, Changil, Chuang, Yung-Yu, Kopf, Johannes, Huang, Jia-Bin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2023
Témata:
3D from multi-view and sensors
Cameras
Computer vision
Dynamics
Heuristic algorithms
Reconstruction algorithms
Robustness
Structure from motion
ISSN:1063-6919
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Shrnutí: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.
ISSN:1063-6919
DOI:10.1109/CVPR52729.2023.00010