HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity

Neural surface reconstruction faces critical challenges in achieving geometrically accurate and visually coherent results under complex real-world conditions. We present a unified framework that simultaneously resolves multi-view radiance inconsistencies, enhances low-textured surface recovery, and preserves fine structural details through three fundamental innovations. First, our SDF-guided visibility factor $\mathbb{V}$ establishes continuous occlusion reasoning to eliminate reflection-induced ambiguities in multi-view supervision. Second, we introduce local geometry constraints via ray-aligned patch analysis $\mathbb{P}$, enforcing planarity in textureless regions while maintaining edge sensitivity through adaptive feature weighting. Third, we reformulate Eikonal regularization with rendering-prioritized relaxation, enabling detail preservation by conditioning geometric smoothness on local radiance variations. Unlike prior works that address these aspects in isolation, our method achieves synergistic optimization where multi-view consistency, surface regularity, and structural fidelity mutually reinforce without compromise. Extensive experiments across synthetic and real-world datasets demonstrate state-of-the-art performance, with quantitative improvements of 21.4\% in Chamfer distance over reflection-aware baselines and 2.32 dB PSNR gains against neural rendering counterparts. Qualitative results showcase unprecedented reconstruction quality for challenging cases including specular instruments, urban layouts with thin structures, and Lambertian surfaces with sub-millimeter details. Our code will be publicly released to facilitate research in unified neural surface recovery.