# A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles

arXiv: https://arxiv.org/abs/2305.19780 · HTML: https://mlsanity.com/paper/2305.19780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 14 of 14)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [On the uncertainty of self-supervised monocular depth estimation](https://mlsanity.com/paper/2005.06209/) (2020)
- [Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow](https://mlsanity.com/paper/1802.07095/) (2018)
- [Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement](https://mlsanity.com/paper/2003.13017/) (2020)
- [Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera](https://mlsanity.com/paper/1901.02571/) (2019)
- [A Benchmark and a Baseline for Robust Multi-view Depth Estimation](https://mlsanity.com/paper/2209.06681/) (2022)
- [ProbFlow: Joint Optical Flow and Uncertainty Estimation](https://mlsanity.com/paper/1708.06509/) (2017)
- [A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo](https://mlsanity.com/paper/2201.07609/) (2022)
- [Deep Multi-view Depth Estimation with Predicted Uncertainty](https://mlsanity.com/paper/2011.09594/) (2020)
- [Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments](https://mlsanity.com/paper/2201.08633/) (2022)

## Abstract (arXiv)

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 .