Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization
This work addresses reconstruction challenges in low-textured areas for computer vision applications, representing an incremental improvement over prior methods.
The paper tackles the problem of reconstructing low-textured areas in multi-view stereo by introducing dual-level precision edge information to enhance plane model robustness and refine sampling strategies, achieving state-of-the-art performance on benchmarks like ETH3D and Tanks & Temples.
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.