POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality

• URL: http://arxiv.org/abs/2503.07819v2
• Date: Tue, 25 Mar 2025 18:08:49 GMT
• Title: POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality
• Authors: Joey Wilson, Marcelino Almeida, Sachit Mahajan, Martin Labrie, Maani Ghaffari, Omid Ghasemalizadeh, Min Sun, Cheng-Hao Kuo, Arnab Sen,
• Abstract summary: 3D-GS has proven to be a useful world model with high-quality computations, but it does not quantify uncertainty or information.<n>We propose to quantify information gain in 3D-GS by reformulating the problem through the lens of optimal experimental design.
• Score: 12.023303901740753
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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