Abstract
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.
Abstract (translated)
3D高斯点阵（Gaussian Splatting）作为一种高效的逼真新视角合成方法已经崭露头角，然而其依赖稀疏的结构从运动（SfM）点云来重建场景的质量始终存在问题。为了解决这些限制，本文提出了一种新的三维重建框架——高斯过程高斯点阵（GP-GS），其中开发了一个多输出高斯过程模型，以实现对稀疏SfM点云的自适应且基于不确定性指导的密度化。 具体来说，我们设计了一条动态采样和过滤流水线，这条管线通过利用基于高斯过程的预测从输入的二维像素和深度图中推断出新的候选点来自适应地扩展SfM点云。该流程使用不确定性的估计来引导去除方差高的预测，确保了几何一致性，并能够生成密集的点云。这些密度化的点云提供了高质量的初始三维高斯分布，以提升重建性能。 在合成和现实世界数据集上进行的各种规模上的广泛实验验证了所提出的框架的有效性和实用性。
URL
https://arxiv.org/abs/2502.02283
