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Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting by Wilson, Joey; Almeida, Marcelino; Sun, Min; Mahajan, Sachit; Ghaffari, Maani; Ewen, Parker; Ghasemalizadeh, Omid; Kuo, Cheng-Hao; Sen, Arnie is a scholarly article available to read on EtoBox.

What is Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting about?

In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhanced scene understanding, 3D-GS can fail without warning which presents a challenge for safety-critical robotic applications. To address this gap, we propose a method which advances the literature of continuous semantic mapping from voxels to ellipsoids, combining the precise structure of 3D-GS with the ability to quantify uncertainty of probabilistic robotic maps. Given a set of images, our algorithm performs a probabilistic semantic update directly on the 3D ellipsoids to obtain an expectation and variance through the use of conjugate priors. We also propose a probabilistic rasterization which returns per-pixel segmentation predictions with quantifiable uncertainty. We compare our method with similar probabilistic voxel-based methods to verify our extension to 3D ellipsoids, and perform ablation studies on uncertainty quantification and temporal smoothing.

Author

Wilson, Joey; Almeida, Marcelino; Sun, Min; Mahajan, Sachit; Ghaffari, Maani; Ewen, Parker; Ghasemalizadeh, Omid; Kuo, Cheng-Hao; Sen, Arnie

Published

2024

Language

EN