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arXiv · 2502.02283

GP-GS: Gaussian Processes Densification for 3D Gaussian Splatting

Zhihao Guo·Jingxuan Su·Chenghao Qian·Shenglin Wang·Jinlong Fan·Jing Zhang·Wei Zhou·Hadi Amirpour·Yunlong Zhao·Liangxiu Han·Peng Wang
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Abstract

3D Gaussian Splatting (3DGS) enables photorealistic rendering but suffers from artefacts due to sparse Structure-from-Motion (SfM) initialisation. To address this limitation, we propose GP-GS, a Gaussian Process (GP) based densification framework for 3DGS optimisation. GP-GS formulates point cloud densification as a continuous regression problem, where a GP learns a local mapping from 2D pixel coordinates to 3D position and colour attributes. An adaptive neighbourhood-based sampling strategy generates candidate pixels for inference, while GP-predicted uncertainty is used to filter unreliable predictions, reducing noise and preserving geometric structure. Extensive experiments on synthetic and real-world benchmarks demonstrate that GP-GS consistently improves reconstruction quality and rendering fidelity, achieving up to 1.12 dB PSNR improvement over strong baselines.

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Zhihao Guo, Jingxuan Su, Chenghao Qian, Shenglin Wang, Jinlong Fan, Jing Zhang, Wei Zhou, Hadi Amirpour, Yunlong Zhao, Liangxiu Han, Peng Wang. 2025-02-04. GP-GS: Gaussian Processes Densification for 3D Gaussian Splatting. https://arxiv.org/abs/2502.02283

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