Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference   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.

34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys