Multiview stereo reconstruction of UAV remote sensing images based on adaptive propagation with multiregional refinement

Based on computer vision and image processing technologies, 3D reconstruction of ground or building targets can be achieved from drone images. However, current algorithms still have significant room for improvement in dense reconstruction of weak-textured areas. In order to enhance the reconstruction effect in weak texture regions, this paper proposes a multi-view stereo method for unmanned aerial vehicle (UAV) remote sensing images based on adaptive propagation and multi-region refinement, called APMRR-MVS. Firstly, in the propagation step, we propose an adaptive propagation strategy based on a checkerboard grid, which expands the distal sampling region by continuously selecting pixels with better hypotheses, that can explore the distal end more flexibly and improve the quality of sampling hypotheses for the pixels within the same view. Secondly, in the refinement step, we propose a multi-region refinement strategy, which can improve the efficiency of exploring the solution space by arranging several regions independently and reduce the possibility of the target pixel hypothesis being trapped in a local optimum. Experiments on relevant datasets show that our method has better performance in reconstructing weakly textured regions, in addition to preserving specific texture details to a greater extent and reducing the misestimation of spatial points.