  We present a learning based approach for multi-view stereopsis (MVS). While
current deep MVS methods achieve impressive results, they crucially rely on
ground-truth 3D training data, and acquisition of such precise 3D geometry for
supervision is a major hurdle. Our framework instead leverages photometric
consistency between multiple views as supervisory signal for learning depth
prediction in a wide baseline MVS setup. However, naively applying photo
consistency constraints is undesirable due to occlusion and lighting changes
across views. To overcome this, we propose a robust loss formulation that: a)
enforces first order consistency and b) for each point, selectively enforces
consistency with some views, thus implicitly handling occlusions. We
demonstrate our ability to learn MVS without 3D supervision using a real
dataset, and show that each component of our proposed robust loss results in a
significant improvement. We qualitatively observe that our reconstructions are
often more complete than the acquired ground truth, further showing the merits
of this approach. Lastly, our learned model generalizes to novel settings, and
our approach allows adaptation of existing CNNs to datasets without
ground-truth 3D by unsupervised finetuning. Project webpage:
https://tejaskhot.github.io/unsup_mvs
