  We present PatchmatchNet, a novel and learnable cascade formulation of
Patchmatch for high-resolution multi-view stereo. With high computation speed
and low memory requirement, PatchmatchNet can process higher resolution imagery
and is more suited to run on resource limited devices than competitors that
employ 3D cost volume regularization. For the first time we introduce an
iterative multi-scale Patchmatch in an end-to-end trainable architecture and
improve the Patchmatch core algorithm with a novel and learned adaptive
propagation and evaluation scheme for each iteration. Extensive experiments
show a very competitive performance and generalization for our method on DTU,
Tanks & Temples and ETH3D, but at a significantly higher efficiency than all
existing top-performing models: at least two and a half times faster than
state-of-the-art methods with twice less memory usage.
