  Deep learning has recently demonstrated its excellent performance for
multi-view stereo (MVS). However, one major limitation of current learned MVS
approaches is the scalability: the memory-consuming cost volume regularization
makes the learned MVS hard to be applied to high-resolution scenes. In this
paper, we introduce a scalable multi-view stereo framework based on the
recurrent neural network. Instead of regularizing the entire 3D cost volume in
one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet)
sequentially regularizes the 2D cost maps along the depth direction via the
gated recurrent unit (GRU). This reduces dramatically the memory consumption
and makes high-resolution reconstruction feasible. We first show the
state-of-the-art performance achieved by the proposed R-MVSNet on the recent
MVS benchmarks. Then, we further demonstrate the scalability of the proposed
method on several large-scale scenarios, where previous learned approaches
often fail due to the memory constraint. Code is available at
https://github.com/YoYo000/MVSNet.
