Abstract Recent advancements in 3D Gaussian-based scene rendering have demonstrated significant potential for efficient neural scene representation. However, accurately capturing geometric boundaries and fine details in complex scenes during optimization remains challenging. Specifically, two key issues persist: (1) the blurring of edge details in high-contrast objects and (2) the loss of texture details in small, distant objects with sparse point cloud distributions. To address these challenges, we propose a method that introduces two key improvements: an anchor re-growing module that dynamically increases neural Gaussian density in high-gradient regions using edge-aware optimization, and the Segment Anything Model (SAM) for generating accurate object segmentation masks to guide segmentation-based loss computation. Our collaborative optimization strategy significantly enhances boundary clarity and texture fidelity in 3D Gaussian rendering, particularly in complex scenes. Evaluated on multiple datasets, our method demonstrates substantial improvements in rendering quality, achieving a 0.29 improvement in PSNR on the Tanks&Temples dataset compared to the baseline method. Project page: https://github.com/Mazycity57/Edge-GS Data availability No datasets were generated or analysed during the current study. References - Azinović, D., Martin-Brualla, R., Goldman, D.B., Nießner, M., Thies, J.: Neural RGB-D surface reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6290–6301. (2022) - Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5470–5479. (2022) - Berger, M., Tagliasacchi, A., Seversky, L.M., Alliez, P., Guennebaud, G., Levine, J.A., Sharf, A., Silva, C.T.: A survey of surface reconstruction from point clouds. Computer Gr. Forum 36, 301–329 (2017) Wiley Online Library - Chen, H., Li, C., Lee, G.H.: Neusg: neural implicit surface reconstruction with 3D gaussian splatting guidance. Preprint at arXiv:2312.00846 (2023) - Chen, Y., Lee, G.H.: Dogaussian: Distributed-oriented gaussian splatting for large-scale