Adaptive Learning for Multi-view Stereo Reconstruction
Qinglu Min Jie Zhao Zhihao Zhang Chen Min
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.