Papers › A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles
Michaël Fonder, Marc Van Droogenbroeck
When used by autonomous vehicles for trajectory planning or obstacle avoidance, depth estimation methods need to be reliable. Therefore, estimating the quality of the depth outputs is critical. In this paper, we show how M4Depth, a state-of-the-art depth estimation method designed for unmanned aerial vehicle (UAV) applications, can be enhanced to perform joint depth and uncertainty estimation. For that, we present a solution to convert the uncertainty estimates related to parallax generated by M4Depth into uncertainty estimates related to depth, and show that it outperforms the standard probabilistic approach. Our experiments on various public datasets demonstrate that our method performs consistently, even in zero-shot transfer. Besides, our method offers a compelling value when compared to existing multi-view depth estimation methods as it performs similarly on a multi-view depth estimation benchmark despite being 2.5 times faster and causal, as opposed to other methods. The code of our method is publicly available at https://github.com/michael-fonder/M4DepthU .
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Depth Aleatoric Uncertainty Estimation | Mid-Air Dataset | M4Depth+U | AuSE on Abs Rel | 0.007 | #1 of 1 | Archive leaderboard | report |
| Depth Aleatoric Uncertainty Estimation | Mid-Air Dataset | M4Depth+U | AuSE on RMSE log | 0.02 | #1 of 1 | Archive leaderboard | report |
| Monocular Depth Estimation | Mid-Air Dataset | M4Depth+U | Abs Rel | 0.134 | #1 of 6 | Archive leaderboard | report |
| Monocular Depth Estimation | Mid-Air Dataset | M4Depth+U | RMSE log | 0.188 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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