论文标题
关于自我监督的单眼深度估计的不确定性
On the uncertainty of self-supervised monocular depth estimation
论文作者
论文摘要
单眼深度估计的自我监督范式非常有吸引力，因为它们根本不需要地面真相注释。尽管这种方法产生了惊人的结果，但学习推理估计深度图的不确定性对于实际应用至关重要，但在文献中却没有教堂。我们是故意的，我们首次探讨了如何估计该任务的不确定性以及这如何影响深度准确性，并提出了一种专门为自我监督方法设计的新颖特殊技术。在标准的Kitti数据集上，我们通过不同的自我监督范式详尽地评估了每种方法的性能。这样的评估强调，我们的建议i）始终提高深度精度，ii）ii）在序列训练和竞争性结果唯一部署立体声对时会产生有关不确定性估计的最先进结果。
Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing results yielded by such methodologies, learning to reason about the uncertainty of the estimated depth maps is of paramount importance for practical applications, yet uncharted in the literature. Purposely, we explore for the first time how to estimate the uncertainty for this task and how this affects depth accuracy, proposing a novel peculiar technique specifically designed for self-supervised approaches. On the standard KITTI dataset, we exhaustively assess the performance of each method with different self-supervised paradigms. Such evaluation highlights that our proposal i) always improves depth accuracy significantly and ii) yields state-of-the-art results concerning uncertainty estimation when training on sequences and competitive results uniquely deploying stereo pairs.
