Deep learning has shown to be effective for depth inference in multi-view
stereo (MVS). However, the scalability and accuracy still remain an open
problem in this domain. This can be attributed to the memory-consuming cost
volume representation and inappropriate depth inference. Inspired by the
group-wise correlation in stereo matching, we propose an average group-wise
correlation similarity measure to construct a lightweight cost volume. This can
not only reduce the memory consumption but also reduce the computational burden
in the cost volume filtering. Based on our effective cost volume
representation, we propose a cascade 3D U-Net module to regularize the cost
volume to further boost the performance. Unlike the previous methods that treat
multi-view depth inference as a depth regression problem or an inverse depth
classification problem, we recast multi-view depth inference as an inverse
depth regression task. This allows our network to achieve sub-pixel estimation
and be applicable to large-scale scenes. Through extensive experiments on DTU
dataset and Tanks and Temples dataset, we show that our proposed network with
Correlation cost volume and Inverse DEpth Regression (CIDER), achieves
state-of-the-art results, demonstrating its superior performance on scalability
and accuracy.

本研究提出了一种用于减少计算负担和对大规模景观适用的轻量级花费体积平均群组相似性度量以及基于此有效的花费体积表示的级联 3D U-Net 模块，并将多视角深度推断重新转换为反深度回归任务，旨在提升深度推断的可扩展性和准确性，在 DTU 数据集和 Tanks and Temples 数据集上实现了方差的估计和最优性能的状态。