Robust Dynamic Radiance Fields
Part Of
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Start Page
13
End Page
23
ISSN
10636919
Date Issued
2023-06
Author(s)
Yu-Lun Liu
Chen Gao
Andreas Meuleman
Hung-Yu Tseng
Ayush Saraf
Changil Kim
Johannes Kopf
Jia-Bin Huang
DOI
10.1109/CVPR52729.2023.00010
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.
Event(s)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
SDGs
Publisher
IEEE
Type
conference paper