  Supervised multi-view stereo (MVS) methods have achieved remarkable progress
in terms of reconstruction quality, but suffer from the challenge of collecting
large-scale ground-truth depth. In this paper, we propose a novel
self-supervised training pipeline for MVS based on knowledge distillation,
termed KD-MVS, which mainly consists of self-supervised teacher training and
distillation-based student training. Specifically, the teacher model is trained
in a self-supervised fashion using both photometric and featuremetric
consistency. Then we distill the knowledge of the teacher model to the student
model through probabilistic knowledge transferring. With the supervision of
validated knowledge, the student model is able to outperform its teacher by a
large margin. Extensive experiments performed on multiple datasets show our
method can even outperform supervised methods.
