Robust Dynamic Radiance Fields

PubDate: June 20, 2023

Teams: Meta 2National Taiwan University 3KAIST 4University of Maryland

Writers: Yu-Lun Liu, Chen Gao, Andreas Meuleman, Hung-Yu Tseng, Ayush Saraf, Changil Kim, Yung-Yu Chuang, Johannes Kopf, Jia-Bin Huang

PDF: Robust Dynamic Radiance Fields

Abstract

Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that ac- curate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are un- reliable 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 robust- ness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods.