Deep learning has recently demonstrated its excellent performance on the task
of multi-view stereo (MVS). However, loss functions applied for deep MVS are
rarely studied. In this paper, we first analyze existing loss functions'
properties for deep depth based MVS approaches. Regression based loss leads to
inaccurate continuous results by computing mathematical expectation, while
classification based loss outputs discretized depth values. To this end, we
then propose a novel loss function, named adaptive Wasserstein loss, which is
able to narrow down the difference between the true and predicted probability
distributions of depth. Besides, a simple but effective offset module is
introduced to better achieve sub-pixel prediction accuracy. Extensive
experiments on different benchmarks, including DTU, Tanks and Temples and
BlendedMVS, show that the proposed method with the adaptive Wasserstein loss
and the offset module achieves state-of-the-art performance.

本文研究了深度学习在多视图立体视觉任务中的优秀性能，并通过分析现有的损失函数，提出了一种新的自适应 Wasserstein 损失函数以及一个简单而有效的偏移模块，通过在不同基准测试中的广泛实验证明，该方法在自适应 Wasserstein 损失和偏移模块的支持下实现了最先进的性能。