3D Gaussian Splatting has emerged as an efficient photorealistic novel view synthesis method. However, its reliance on sparse Structure-from-Motion (SfM) point clouds consistently compromises the scene reconstruction quality. To address these limitations, this paper proposes a novel 3D reconstruction framework Gaussian Processes Gaussian Splatting (GP-GS), where a multi-output Gaussian Process model is developed to achieve adaptive and uncertainty-guided densification of sparse SfM point clouds. Specifically, we propose a dynamic sampling and filtering pipeline that adaptively expands the SfM point clouds by leveraging GP-based predictions to infer new candidate points from the input 2D pixels and depth maps. The pipeline utilizes uncertainty estimates to guide the pruning of high-variance predictions, ensuring geometric consistency and enabling the generation of dense point clouds. The densified point clouds provide high-quality initial 3D Gaussians to enhance reconstruction performance. Extensive experiments conducted on synthetic and real-world datasets across various scales validate the effectiveness and practicality of the proposed framework.

翻译：三维高斯泼溅已成为一种高效的光照真实感新视角合成方法。然而，其依赖于稀疏的运动恢复结构点云，始终会损害场景重建质量。为应对这些局限，本文提出了一种新颖的三维重建框架——高斯过程高斯泼溅，其中开发了一个多输出高斯过程模型，以实现对稀疏SfM点云的自适应且不确定性引导的致密化。具体而言，我们提出了一种动态采样与过滤流程，该流程通过利用基于高斯过程的预测，从输入的二维像素与深度图中推断新的候选点，从而自适应地扩展SfM点云。该流程利用不确定性估计来指导高方差预测的剪枝，确保几何一致性并实现稠密点云的生成。致密化的点云为三维高斯提供了高质量的初始化，从而提升了重建性能。在多种尺度的合成与真实数据集上进行的大量实验验证了所提框架的有效性与实用性。