GS-Mesh: Implicit Surface Bridging for Collaborative Gaussian-Mesh Reconstruction

3D Gaussian Splatting enables high-quality novel-view synthesis and real-time rendering, but its discrete primitives do not directly provide explicit and continuous surface representations. Existing methods either extract meshes through post-processing, preventing mesh feedback to Gaussians, or introduce additional geometric representations that require explicit coupling. To address these limitations, we propose GS-Mesh, a collaborative Gaussian-mesh reconstruction framework based on implicit surface bridging. Our key observation is that surface-aligned Gaussians naturally form attribute-rich oriented surface samples, whose positions, rotations, and opacities can directly induce a continuous implicit surface. Based on this observation, we instantiate Implicit Moving Least Squares (IMLS) on Gaussian primitives and combine it with differentiable surface extraction and mesh rendering, establishing a Gaussian-native implicit bridge for collaborative Gaussian-mesh optimization. We further introduce IMLS-guided densification to improve surface support in under-sampled regions and coarse-to-fine surface optimization to progressively recover coherent geometry and fine details. Experiments on standard surface reconstruction and novel-view synthesis benchmarks demonstrate accurate and detailed mesh reconstruction while retaining competitive rendering quality. The code will be made publicly available.