GenS is an end-to-end generalizable neural surface reconstruction model that combines the signed distance function (SDF) and differentiable volume rendering to reconstruct surfaces from multi-view images without 3D supervision. Unlike coordinate-based methods, GenS constructs a generalized multi-scale volume to encode all scenes, enabling it to recover high-frequency details while maintaining global smoothness. The model introduces a multi-scale feature-metric consistency to enforce multi-view consistency in a more discriminative multi-scale feature space, robust to photometric consistency failures. Additionally, a view contrast loss is designed to improve reconstruction accuracy in regions with few viewpoints. Extensive experiments on the DTU and BlendedMVS datasets demonstrate that GenS generalizes well to new scenes and outperforms existing state-of-the-art methods, even those using ground-truth depth supervision. The model's effectiveness is further validated through ablation studies, showing that each component contributes significantly to its overall performance.GenS is an end-to-end generalizable neural surface reconstruction model that combines the signed distance function (SDF) and differentiable volume rendering to reconstruct surfaces from multi-view images without 3D supervision. Unlike coordinate-based methods, GenS constructs a generalized multi-scale volume to encode all scenes, enabling it to recover high-frequency details while maintaining global smoothness. The model introduces a multi-scale feature-metric consistency to enforce multi-view consistency in a more discriminative multi-scale feature space, robust to photometric consistency failures. Additionally, a view contrast loss is designed to improve reconstruction accuracy in regions with few viewpoints. Extensive experiments on the DTU and BlendedMVS datasets demonstrate that GenS generalizes well to new scenes and outperforms existing state-of-the-art methods, even those using ground-truth depth supervision. The model's effectiveness is further validated through ablation studies, showing that each component contributes significantly to its overall performance.