GP-GS: Gaussian Processes for Enhanced Gaussian Splatting

• URL: http://arxiv.org/abs/2502.02283v2
• Date: Wed, 05 Feb 2025 16:09:26 GMT
• Title: GP-GS: Gaussian Processes for Enhanced Gaussian Splatting
• Authors: Zhihao Guo, Jingxuan Su, Shenglin Wang, Jinlong Fan, Jing Zhang, Liangxiu Han, Peng Wang,
• Abstract summary: This paper proposes a novel 3D reconstruction framework that achieves adaptive and uncertainty-guided densification of sparse SfM point clouds. The pipeline utilizes uncertainty estimates to guide the pruning of high-variance predictions. Experiments conducted on synthetic and real-world datasets validate the effectiveness and practicality of the proposed framework.
• Score: 10.45038376276218
• License:
• 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.

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