GP-GS: Gaussian Processes for Enhanced Gaussian Splatting

• URL: http://arxiv.org/abs/2502.02283v5
• Date: Tue, 13 May 2025 15:14:37 GMT
• Title: GP-GS: Gaussian Processes for Enhanced Gaussian Splatting
• Authors: Zhihao Guo, Jingxuan Su, Shenglin Wang, Jinlong Fan, Jing Zhang, Wei Zhou, Hadi Amirpour, Yunlong Zhao, Liangxiu Han, Peng Wang,
• Abstract summary: This paper proposes a novel 3D reconstruction framework, Gaussian Processes enhanced Gaussian Splatting (GP-GS)<n>GP-GS enables adaptive and uncertainty-guided densification of sparse Structure-from-Motion point clouds.<n>Experiments conducted on synthetic and real-world datasets validate the effectiveness and practicality of the proposed framework.
• Score: 15.263608848427136
• License: http://creativecommons.org/licenses/by/4.0/
• 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 often limits scene reconstruction quality. To address the limitation, this paper proposes a novel 3D reconstruction framework, Gaussian Processes enhanced Gaussian Splatting (GP-GS), in which a multi-output Gaussian Process model is developed to enable 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. These densified point clouds provide high-quality initial 3D Gaussians, enhancing reconstruction performance. Extensive experiments conducted on synthetic and real-world datasets across various scales validate the effectiveness and practicality of the proposed framework.

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