论文标题
不确定性的新分布排名损失：以相对深度估计进行说明
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation
论文作者
论文摘要
我们为从单个图像中的相对深度估计问题提出了一种新方法。我们没有直接回归深度分数，而是提出问题，作为对深度概率分布的估计，并旨在学习最大化给定数据可能性的分布参数。为了训练我们的模型，我们提出了一种新的排名损失，分配损失，该损失试图增加更远的像素深度的可能性大于较近的像素的深度。我们提出的方法使我们的模型能够以分布的标准偏差的形式对其估计的估计产生信心。我们针对许多基线实现了最新的结果，同时对我们的估计提供了信心。我们的分析表明，估计的置信度实际上是准确性的良好指标。我们在指标深度估计的下游任务中调查了信心信息的使用，以提高其性能。
We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation of a probability distribution over depth and aim to learn the parameters of the distributions which maximize the likelihood of the given data. To train our model, we propose a new ranking loss, Distributional Loss, which tries to increase the probability of farther pixel's depth being greater than the closer pixel's depth. Our proposed approach allows our model to output confidence in its estimation in the form of standard deviation of the distribution. We achieve state of the art results against a number of baselines while providing confidence in our estimations. Our analysis show that estimated confidence is actually a good indicator of accuracy. We investigate the usage of confidence information in a downstream task of metric depth estimation, to increase its performance.
