UAV向け深度と深度不確かさの同時推定手法
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles
UAV向けの深度推定手法M4Depthを拡張し、視差の不確かさから深度の不確かさを推定する方法を提案。公開データセットで有効性を示し、既存手法より2.5倍高速で因果的。
著者: Michaël Fonder, Marc Van Droogenbroeck
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
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 .