The paper introduces GSDF, a novel dual-branch architecture that combines 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF) to improve rendering and reconstruction quality. The core idea is to leverage the strengths of both branches while alleviating their limitations through mutual guidance and joint supervision. The authors address the challenges in neural scene rendering and reconstruction, such as the lack of explicit geometry in neural representations and the degradation of rendering quality due to constraints on density fields or primitive shapes. GSDF enhances the efficiency and fidelity of rendering with Gaussian primitives and improves the accuracy and detail of surface reconstructions by aligning the density distribution with the distance function. Extensive experiments on diverse scenes demonstrate that GSDF achieves more accurate and detailed surface reconstructions and benefits 3DGS rendering with structures that better align with the underlying geometry. The method is evaluated on various datasets and compared against state-of-the-art methods, showing superior performance in both rendering and reconstruction tasks.The paper introduces GSDF, a novel dual-branch architecture that combines 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF) to improve rendering and reconstruction quality. The core idea is to leverage the strengths of both branches while alleviating their limitations through mutual guidance and joint supervision. The authors address the challenges in neural scene rendering and reconstruction, such as the lack of explicit geometry in neural representations and the degradation of rendering quality due to constraints on density fields or primitive shapes. GSDF enhances the efficiency and fidelity of rendering with Gaussian primitives and improves the accuracy and detail of surface reconstructions by aligning the density distribution with the distance function. Extensive experiments on diverse scenes demonstrate that GSDF achieves more accurate and detailed surface reconstructions and benefits 3DGS rendering with structures that better align with the underlying geometry. The method is evaluated on various datasets and compared against state-of-the-art methods, showing superior performance in both rendering and reconstruction tasks.