In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to be a useful world model with high-quality rasterizations, it does not natively quantify uncertainty or information, posing a challenge for real-world applications such as 3D-GS SLAM. We propose to quantify information gain in 3D-GS by reformulating the problem through the lens of optimal experimental design, which is a classical solution widely used in literature. By restructuring information quantification of 3D-GS through optimal experimental design, we arrive at multiple solutions, of which T-Optimality and D-Optimality perform the best quantitatively and qualitatively as measured on two popular datasets. Additionally, we propose a block diagonal covariance approximation which provides a measure of correlation at the expense of a greater computation cost.
View on arXiv@article{wilson2025_2503.07819,
title={ POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality },
author={ Joey Wilson and Marcelino Almeida and Sachit Mahajan and Martin Labrie and Maani Ghaffari and Omid Ghasemalizadeh and Min Sun and Cheng-Hao Kuo and Arnab Sen },
journal={arXiv preprint arXiv:2503.07819},
year={ 2025 }
}