GeoTexDensifier: Geometry-Texture-Aware Densification for High-Quality Photorealistic 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has recently attracted wide attentions in various areas such as 3D navigation, Virtual Reality (VR) and 3D simulation, due to its photorealistic and efficient rendering performance. High-quality reconstrution of 3DGS relies on sufficient splats and a reasonable distribution of these splats to fit real geometric surface and texture details, which turns out to be a challenging problem. We present GeoTexDensifier, a novel geometry-texture-aware densification strategy to reconstruct high-quality Gaussian splats which better comply with the geometric structure and texture richness of the scene. Specifically, our GeoTexDensifier framework carries out an auxiliary texture-aware densification method to produce a denser distribution of splats in fully textured areas, while keeping sparsity in low-texture regions to maintain the quality of Gaussian point cloud. Meanwhile, a geometry-aware splitting strategy takes depth and normal priors to guide the splitting sampling and filter out the noisy splats whose initial positions are far from the actual geometric surfaces they aim to fit, under a Validation of Depth Ratio Change checking. With the help of relative monocular depth prior, such geometry-aware validation can effectively reduce the influence of scattered Gaussians to the final rendering quality, especially in regions with weak textures or without sufficient training views. The texture-aware densification and geometry-aware splitting strategies are fully combined to obtain a set of high-quality Gaussian splats. We experiment our GeoTexDensifier framework on various datasets and compare our Novel View Synthesis results to other state-of-the-art 3DGS approaches, with detailed quantitative and qualitative evaluations to demonstrate the effectiveness of our method in producing more photorealistic 3DGS models.
3D高斯点绘（3D Gaussian Splatting, 3DGS）因其逼真且高效的渲染性能，近年来在3D导航、虚拟现实（VR）和3D模拟等领域广受关注。高质量的3DGS重建依赖于足够的点分布以及合理的点密度，以匹配真实的几何表面和纹理细节，这一过程往往面临诸多挑战。 我们提出了GeoTexDensifier，一种新颖的几何-纹理感知的密集化策略，用于重建高质量的高斯点分布，更好地符合场景的几何结构和纹理丰富性。具体来说，GeoTexDensifier 框架采用辅助的纹理感知密集化方法，在纹理丰富的区域生成更高密度的点分布，同时在低纹理区域保持稀疏分布，从而保证高斯点云的整体质量。同时，我们设计了一种几何感知分裂策略，通过深度和法线的先验信息指导分裂采样，并利用深度比率变化验证（Validation of Depth Ratio Change）筛除初始位置远离实际几何表面的噪声点。 借助相对单目深度先验，这种几何感知验证方法能有效减少离散高斯点对最终渲染质量的影响，特别是在纹理较弱或缺乏充分训练视角的区域。纹理感知密集化与几何感知分裂策略的结合，最终生成了一组高质量的高斯点。 我们在多个数据集上对 GeoTexDensifier 框架进行了实验，并将其新视角合成结果与其他最先进的3DGS方法进行比较，通过详尽的定量和定性评估展示了我们方法在生成更逼真3DGS模型方面的有效性。