  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 .
