Abstract
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 highresolution 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.