Uncertainty Aware Multiple View Stereo Network with Accurate Supervision

Learning-based multiple view stereo has gained significant attention recently. However, most methods rely on direct network supervision using provided ground-truth depth, which poses three inherent problems: resolution-dependent ground-truth artifacts, excessively challenging training examples (with relatively featureless textures), and use of less-viewed reference pixels for supervision, all of which hinder network optimization. To alleviate these problems, we propose an accurate network supervision paradigm that includes a ground-truth mask, an entropy mask, and a consistency mask, which provide more accurate supervision signals to aid network optimization. Furthermore, we introduce UANet, an uncertainty aware multi-view stereo network, which adaptively determines a pixel-wise search range using a dynamic range sampler (DRS) built upon estimation confidence and learned uncertainty. Experimental results on recent MVS datasets demonstrate the effectiveness of our method.