With the widespread application of 3D visualization in digital exhibition halls and virtual reality, achieving efficient rendering and high-fidelity presentation has become a key challenge. This study proposes a hybrid point cloud generation method that combines traditional sampling with 3D Gaussian splatting, aiming to address the issues of rendering delay and missing details in existing 3D displays. By improving the OBJ model parsing process and incorporating an adaptive area-weighted sampling algorithm, we achieve adaptive point cloud generation based on triangle density. Innovatively, we advance the ellipsoidal parameter estimation process of 3D Gaussian splatting to the point cloud generation stage. By establishing a mathematical relationship between the covariance matrix and local curvature, the generated point cloud naturally exhibits Gaussian distribution characteristics. Experimental results show that, compared to traditional methods, our approach reduces point cloud data by 38% while maintaining equivalent visual quality at a 4096×4096 texture resolution. By introducing mipmap texture optimization strategies and a GPU-accelerated rasterization pipeline, stable rendering at 60 frames per second is achieved in a WebGL environment. Additionally, we quantize and compress the spherical harmonic function parameters specific to 3D Gaussian splatting, reducing network transmission bandwidth to 52% of the original data. This study provides a new technical pathway for fields requiring high-precision display, such as the digitization of cultural heritage.