mlsanityData to 7 Oct 2026

DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow

arXiv 2304.02569 ↗First posted 5 Apr 2023cs.CVcited by 1

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Abstract (arXiv)

Existing work on scene flow estimation focuses on autonomous driving and mobile robotics, while automated solutions are lacking for motion in nature, such as that exhibited by debris flows. We propose DEFLOW, a model for 3D motion estimation of debris flows, together with a newly captured dataset. We adopt a novel multi-level sensor fusion architecture and self-supervision to incorporate the inductive biases of the scene. We further adopt a multi-frame temporal processing module to enable flow speed estimation over time. Our model achieves state-of-the-art optical flow and depth estimation on our dataset, and fully automates the motion estimation for debris flows. The source code and dataset are available at project page.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 63 references that are in the map. First posted on arXiv 5 Apr 2023.

Builds on

30 of 63 references.

References · 30

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18 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025HiMo: High-Speed Objects Motion Compensation in Point Cloudscited by 11