  Research on multi-view stereo based on remote sensing images has promoted the
development of large-scale urban 3D reconstruction. However, remote sensing
multi-view image data suffers from the problems of occlusion and uneven
brightness between views during acquisition, which leads to the problem of
blurred details in depth estimation. To solve the above problem, we re-examine
the deformable learning method in the Multi-View Stereo task and propose a
novel paradigm based on view Space and Depth deformable Learning (SDL-MVS),
aiming to learn deformable interactions of features in different view spaces
and deformably model the depth ranges and intervals to enable high accurate
depth estimation. Specifically, to solve the problem of view noise caused by
occlusion and uneven brightness, we propose a Progressive Space deformable
Sampling (PSS) mechanism, which performs deformable learning of sampling points
in the 3D frustum space and the 2D image space in a progressive manner to embed
source features to the reference feature adaptively. To further optimize the
depth, we introduce Depth Hypothesis deformable Discretization (DHD), which
achieves precise positioning of the depth prior by adaptively adjusting the
depth range hypothesis and performing deformable discretization of the depth
interval hypothesis. Finally, our SDL-MVS achieves explicit modeling of
occlusion and uneven brightness faced in multi-view stereo through the
deformable learning paradigm of view space and depth, achieving accurate
multi-view depth estimation. Extensive experiments on LuoJia-MVS and WHU
datasets show that our SDL-MVS reaches state-of-the-art performance. It is
worth noting that our SDL-MVS achieves an MAE error of 0.086, an accuracy of
98.9% for <0.6m, and 98.9% for <3-interval on the LuoJia-MVS dataset under the
premise of three views as input.
