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RoDynRF: Robust Dynamic Radiance Fields

In this work, the authors address the robustness issue of dynamic radiance field reconstruction methods by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). Learn how they do it!

Dmytro Spodarets
Dmytro Spodarets
Jan 20, 2023 · 1 min read
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RoDynRF: Robust Dynamic Radiance Fields

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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.

RoDynRF

RoDynRF addresses the robustness issue of SfM algorithms by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length).

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RoDynRF takes a casually captured video as input and reconstructs the camera trajectory and dynamic radiance fields. Conventional SfM system such as COLMAP fails to recover camera poses (even when using ground truth motion masks). As a result, existing dynamic radiance field methods that require accurate pose estimation are inapplicable to these challenging dynamic scenes. RoDynRF tackles this robustness problem and showcases high-fidelity dynamic view synthesis results on a wide variety of videos.
Dmytro Spodarets
Dmytro Spodarets
Founder & Editor-in-Chief

Founder and Chief Editor of Data Phoenix — a San Francisco Bay Area media and education platform focused on AI and Data.

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