Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantification of the prediction confidence is crucial to guide decision-making systems that rely on depth estimation. In this paper, we propose MonoProb, a new unsupervised monocular depth estimation method that returns an interpretable uncertainty, which means that the uncertainty reflects the expected error of the network in its depth predictions. We rethink the stereo or the structure-from-motion paradigms used to train unsupervised monocular depth models as a probabilistic problem. Within a single forward pass inference, this model provides a depth prediction and a measure of its confidence, without increasing the inference time. We then improve the performance on depth and uncertainty with a novel self-distillation loss for which a student is supervised by a pseudo ground truth that is a probability distribution on depth output by a teacher. To quantify the performance of our models we design new metrics that, unlike traditional ones, measure the absolute performance of uncertainty predictions. Our experiments highlight enhancements achieved by our method on standard depth and uncertainty metrics as well as on our tailored metrics. https://github.com/CEA-LIST/MonoProb

翻译：自监督单目深度估计方法旨在用于自动驾驶等关键应用中的环境分析。为了规避这些方法的潜在缺陷，量化预测置信度对于依赖深度估计的决策系统至关重要。本文提出MonoProb，一种新的无监督单目深度估计方法，该方法输出可解释的不确定性，即不确定性反映了网络深度预测中的预期误差。我们将用于训练无监督单目深度模型的立体视觉或运动恢复结构范式重新定义为概率问题。在单次前向推理中，该模型提供深度预测及其置信度度量，且不增加推理时间。随后，我们通过一种新的自蒸馏损失来提升深度和不确定性性能，其中学生网络由教师网络输出的深度概率分布伪真值监督。为量化模型性能，我们设计了新的指标，与传统指标不同，这些指标衡量不确定性预测的绝对性能。实验表明，我们的方法在标准深度和不确定性指标以及定制指标上均取得了显著改进。https://github.com/CEA-LIST/MonoProb