In this paper, we propose an efficient multi-scale geometric consistency guided multi-view stereo method for accurate and complete depth map estimation. We first present our basic multi-view stereo method with Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH). It leverages structured region information to sample better candidate hypotheses for propagation and infer the aggregation view subset at each pixel. For the depth estimation of low-textured areas, we further propose to combine ACMH with multi-scale geometric consistency guidance (ACMM) to obtain the reliable depth estimates for low-textured areas at coarser scales and guarantee that they can be propagated to finer scales. To correct the erroneous estimates propagated from the coarser scales, we present a novel detail restorer. Experiments on extensive datasets show our method achieves state-of-the-art performance, recovering the depth estimation not only in low-textured areas but also in details.
Although some methods focus on view selection to improve local smoothness and gain some benefits, they are still restricted by patch window size. To perceive more useful information in low-textured areas, Wei al @cite_12 adopt the multi-scale patch matching with variance based consistency. However, this consistency is too strong to spread some reliable estimates in few neighboring views across multiple views. Moreover, they overlook the errors in details.
@cite_12
2039286679
)# 1.790 1.722 1.626 1.602 1.092 1.179 1.099 Mean Rel. Error of LP+HF+CVF on Pixels of Other Methods ( 10 3)# 1.102 1.068 1.142 1.292 1.319 1.368