Learning-based multi-view stereo (MVS) methods deal with predicting accurate
depth maps to achieve an accurate and complete 3D representation. Despite the
excellent performance, existing methods ignore the fact that a suitable depth
geometry is also critical in MVS. In this paper, we demonstrate that different
depth geometries have significant performance gaps, even using the same depth
prediction error. Therefore, we introduce an ideal depth geometry composed of
Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward
around the ground-truth surface, rather than maintaining a continuous and
smooth depth plane. To achieve it, we develop a coarse-to-fine framework called
Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane.
Technically, we predict two depth values for each pixel (Dual-Depth), and
propose a novel loss function and a checkerboard-shaped selecting strategy to
constrain the predicted depth geometry. Compared to existing methods,DMVSNet
achieves a high rank on the DTU benchmark and obtains the top performance on
challenging scenes of Tanks and Temples, demonstrating its strong performance
and generalization ability. Our method also points to a new research direction
for considering depth geometry in MVS.

通过引入一个由马鞍形细胞构成的理想深度几何结构，本文提出了一种粗到细的深度标定模型（Dual-MVSNet），该模型能够产生一个与真实地表上下波动的深度平面；与已有方法相比，Dual-MVSNet 在 DTU 基准测试中获得了较高的排名，并在涉及坦克和寺庙的具有挑战性场景中取得了最佳的性能，展示了其强大的性能和泛化能力。我们的方法也指出了在多视图立体测量中考虑深度几何的新研究方向。

约束多视角立体的深度图几何：具有鞍状深度单元的双深度方法

Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth  Approach with Saddle-shaped Depth Cells

This paper proposes an end-to-end learning framework for multiview
stereopsis. We term the network SurfaceNet. It takes a set of images and their
corresponding camera parameters as input and directly infers the 3D model. The
key advantage of the framework is that both photo-consistency as well geometric
relations of the surface structure can be directly learned for the purpose of
multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D
convolutional network which is achieved by encoding the camera parameters
together with the images in a 3D voxel representation. We evaluate SurfaceNet
on the large-scale DTU benchmark.

该论文提出了一种名为 SurfaceNet 的端到端学习框架，可用于多视图立体成像中的 3D 建模，并通过大规模 DTU 基准测试对 SurfaceNet 进行了验证。