Time of the Flight of the Gaussians:
Optimizing Depth Indirectly in Dynamic Radiance Fields

CVPR 2025 (Oral Presentation)

Meet us at:
Oral Session 5C — Sun 15 Jun 9:30-9:45 a.m. CDT, Davidson Ballroom
Poster Session 5, #85 — Sun 15 Jun 10:30 a.m.-12:30 p.m. CDT, ExHall D

Runfeng Li Mikhail Okunev Zixuan Guo Anh Ha Duong Christian Richardt Matthew O'Toole James Tompkin

Paper

arXiv

Code (Coming soon)

Models (Coming soon)

Data (Coming soon)

Abstract

We present a method to reconstruct dynamic scenes from monocular continuous-wave time-of-flight (C-ToF) cameras using raw sensor samples that achieves similar or better accuracy than neural volumetric approaches and is 100x faster. Quickly achieving high-fidelity dynamic 3D reconstruction from a single viewpoint is a significant challenge in computer vision. In C-ToF radiance field reconstruction, the property of interest—depth—is not directly measured, causing an additional challenge. This problem has a large and underappreciated impact upon the optimization when using a fast primitive-based scene representation like 3D Gaussian splatting, which is commonly used with multi-view data to produce satisfactory results and is brittle in its optimization otherwise. We incorporate two heuristics into the optimization to improve the accuracy of scene geometry represented by Gaussians. Experimental results show that our approach produces accurate reconstructions under constrained C-ToF sensing conditions, including for fast motions like swinging baseball bats.

Video
Download, MP4 (48.6MB)

BibTex

@inproceedings{li2025gftorf,
    title={Time of the Flight of the {Gaussians}: Optimizing Depth Indirectly in Dynamic Radiance Fields},
    author={Li, Runfeng and Okunev, Mikhail and Guo, Zixuan and Duong, Anh Ha 
        and Richardt, Christian and O'Toole, Matthew and Tompkin, James},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}
    year={2025},
}

Results

Acknowledgements