Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization

TL;DR

This paper introduces DPE-MVS, a multi-view stereo method that uses dual-level edge information to improve low-texture area reconstruction, achieving state-of-the-art results on ETH3D and Tanks & Temples benchmarks.

Contribution

The paper proposes a novel dual-level edge-guided approach to enhance plane model construction and adaptive patch sampling in MVS, improving robustness and accuracy.

Findings

Outperforms all published methods on ETH3D benchmark.

Achieves state-of-the-art performance on Tanks & Temples.

Enhances low-texture area reconstruction robustness.

Abstract

The reconstruction of low-textured areas is a prominent research focus in multi-view stereo (MVS). In recent years, traditional MVS methods have performed exceptionally well in reconstructing low-textured areas by constructing plane models. However, these methods often encounter issues such as crossing object boundaries and limited perception ranges, which undermine the robustness of plane model construction. Building on previous work (APD-MVS), we propose the DPE-MVS method. By introducing dual-level precision edge information, including fine and coarse edges, we enhance the robustness of plane model construction, thereby improving reconstruction accuracy in low-textured areas. Furthermore, by leveraging edge information, we refine the sampling strategy in conventional PatchMatch MVS and propose an adaptive patch size adjustment approach to optimize matching cost calculation in both stochastic and low-textured areas. This additional use of edge information allows for more precise and robust matching. Our method achieves state-of-the-art performance on the ETH3D and Tanks & Temples benchmarks. Notably, our method outperforms all published methods on the ETH3D benchmark.