The paper introduces 3DGSR, an efficient method for high-quality surface reconstruction using 3D Gaussian Splatting (3DG). The key contributions of 3DGSR include: 1. **Implicit Signed Distance Field (SDF) Integration**: The method integrates a neural implicit SDF within 3D Gaussians, allowing for the joint optimization of SDF and 3D Gaussians. A differentiable SDF-to-opacity transformation function is introduced to connect the SDF and 3D Gaussians, enforcing surface constraints and facilitating unified optimization. 2. **Volumetric Rendering and Consistency Loss**: To address the limitations of sparse supervision from 3D Gaussians, volumetric rendering is incorporated to generate depth and normal maps. These maps are then aligned with those derived from 3D Gaussians using a consistency loss, effectively regularizing the SDF in areas not covered by Gaussians. 3. **Performance and Quality**: Extensive experiments on various datasets demonstrate that 3DGSR achieves high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DG. The method outperforms state-of-the-art techniques in both surface reconstruction and novel view synthesis tasks, offering a more efficient learning process and better rendering qualities. 4. **Ablation Study**: The effectiveness of each component of 3DGSR is evaluated through ablation studies, showing that the integration of SDF and 3D Gaussians, as well as the use of volumetric rendering and consistency loss, significantly improve the reconstruction quality and rendering performance. Overall, 3DGSR provides a robust and efficient approach for high-quality 3D surface reconstruction, making it a valuable tool for various geometry-related applications.The paper introduces 3DGSR, an efficient method for high-quality surface reconstruction using 3D Gaussian Splatting (3DG). The key contributions of 3DGSR include: 1. **Implicit Signed Distance Field (SDF) Integration**: The method integrates a neural implicit SDF within 3D Gaussians, allowing for the joint optimization of SDF and 3D Gaussians. A differentiable SDF-to-opacity transformation function is introduced to connect the SDF and 3D Gaussians, enforcing surface constraints and facilitating unified optimization. 2. **Volumetric Rendering and Consistency Loss**: To address the limitations of sparse supervision from 3D Gaussians, volumetric rendering is incorporated to generate depth and normal maps. These maps are then aligned with those derived from 3D Gaussians using a consistency loss, effectively regularizing the SDF in areas not covered by Gaussians. 3. **Performance and Quality**: Extensive experiments on various datasets demonstrate that 3DGSR achieves high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DG. The method outperforms state-of-the-art techniques in both surface reconstruction and novel view synthesis tasks, offering a more efficient learning process and better rendering qualities. 4. **Ablation Study**: The effectiveness of each component of 3DGSR is evaluated through ablation studies, showing that the integration of SDF and 3D Gaussians, as well as the use of volumetric rendering and consistency loss, significantly improve the reconstruction quality and rendering performance. Overall, 3DGSR provides a robust and efficient approach for high-quality 3D surface reconstruction, making it a valuable tool for various geometry-related applications.