Semi-supervised Deep Multi-view Stereo

Significant progress has been witnessed in learning-based Multi-view Stereo (MVS) of supervised and unsupervised settings. To combine their respective merits in accuracy and completeness, meantime reducing the demand for expensive labeled data, this paper explores a novel semi-supervised setting of learning-based MVS problem that only a tiny part of the MVS data is attached with dense depth ground truth. However, due to huge variation of scenarios and flexible setting in views, semi-supervised MVS problem (Semi-MVS) may break the basic assumption in classic semi-supervised learning, that unlabeled data and labeled data share the same label space and data distribution. To handle these issues, we propose a novel semi-supervised MVS framework, namely SE-MVS. For the simple case that the basic assumption works in MVS data, consistency regularization encourages the model predictions to be consistent between original sample and randomly augmented sample via constraints on KL divergence. For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap. The visual style of unlabeled sample is transferred to labeled sample to shrink the gap, and the model prediction of generated sample is further supervised with the label in original labeled sample. The experimental results on DTU, BlendedMVS, GTA-SFM, and Tanks\&Temples datasets show the superior performance of the proposed method. With the same settings in backbone network, our proposed SE-MVS outperforms its fully-supervised and unsupervised baselines.