Neural Radiance Fields (NeRF) have led to substantial advancements in 3D content generation and rendering techniques. Among these, 3D Gaussian Splatting (3DGS) has become a pivotal approach in computer vision, specifically for scene reconstruction and representation. This paper addresses critical challenges within the 3D Gaussian Splatting algorithm, primarily focusing on issues related to insufficient detail and geometric inaccuracies. We introduce a novel rendering method, Rasterization-Enhanced and Geometric-Guided Gaussian Splatting (RG-GS), which combines enhanced rasterization and geometric guidance to address these limitations. Our approach efficiently approximates ellipses in Gaussian rasterization using area-similar and shape-similar tiles, reducing computational costs while maintaining fine details. Additionally, we incorporate depth information into 3DGS by employing a depth map as a global geometric supervisory signal, guiding the training process to improve geometric reconstruction accuracy. Experimental results demonstrate that our method substantially improves fine texture handling, delivering more vibrant and detailed colors with realistic lighting effects, all while minimizing geometric errors.