MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo

TL;DR

This paper introduces MP-MVS, a multi-view stereo method that improves 3D reconstruction in untextured areas using multi-scale PatchMatch, enhanced sampling, and geometric planar priors, achieving state-of-the-art results.

Contribution

It presents a novel multi-scale windows PatchMatch, improved sampling schemes, and a geometric prior-based PatchMatch, advancing accuracy and efficiency in untextured area reconstruction.

Findings

Achieves state-of-the-art results on ETH3D benchmark.

Faster and more extendable than existing multi-scale approaches.

Effectively reconstructs untextured regions with improved accuracy.

Abstract

Significant strides have been made in enhancing the accuracy of Multi-View Stereo (MVS)-based 3D reconstruction. However, untextured areas with unstable photometric consistency often remain incompletely reconstructed. In this paper, we propose a resilient and effective multi-view stereo approach (MP-MVS). We design a multi-scale windows PatchMatch (mPM) to obtain reliable depth of untextured areas. In contrast with other multi-scale approaches, which is faster and can be easily extended to PatchMatch-based MVS approaches. Subsequently, we improve the existing checkerboard sampling schemes by limiting our sampling to distant regions, which can effectively improve the efficiency of spatial propagation while mitigating outlier generation. Finally, we introduce and improve planar prior assisted PatchMatch of ACMP. Instead of relying on photometric consistency, we utilize geometric consistency information between multi-views to select reliable triangulated vertices. This strategy can obtain a more accurate planar prior model to rectify photometric consistency measurements. Our approach has been tested on the ETH3D High-res multi-view benchmark with several state-of-the-art approaches. The results demonstrate that our approach can reach the state-of-the-art. The associated codes will be accessible at https://github.com/RongxuanTan/MP-MVS.