We introduce Point-MVSNet, a novel point-based deep framework for multi-view
stereo (MVS). Distinct from existing cost volume approaches, our method
directly processes the target scene as point clouds. More specifically, our
method predicts the depth in a coarse-to-fine manner. We first generate a
coarse depth map, convert it into a point cloud and refine the point cloud
iteratively by estimating the residual between the depth of the current
iteration and that of the ground truth. Our network leverages 3D geometry
priors and 2D texture information jointly and effectively by fusing them into a
feature-augmented point cloud, and processes the point cloud to estimate the 3D
flow for each point. This point-based architecture allows higher accuracy, more
computational efficiency and more flexibility than cost-volume-based
counterparts. Experimental results show that our approach achieves a
significant improvement in reconstruction quality compared with
state-of-the-art methods on the DTU and the Tanks and Temples dataset. Our
source code and trained models are available at
this https URL .

Point-MVSNet 是用 point clouds 直接处理目标场景的深度学习网络，通过将 3D 几何先验和 2D 纹理信息融入特征增强的 point cloud 中，实现了在 multi-view stereo 中更高的精度、更高的计算效率和更大的灵活性。