Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting

• URL: http://arxiv.org/abs/2411.02547v1
• Date: Mon, 04 Nov 2024 19:31:03 GMT
• Title: Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting
• Authors: Joey Wilson, Marcelino Almeida, Min Sun, Sachit Mahajan, Maani Ghaffari, Parker Ewen, Omid Ghasemalizadeh, Cheng-Hao Kuo, Arnie Sen,
• Abstract summary: We present a novel algorithm for probabilistically updating semantic maps within 3D Gaussian Splatting (3D-GS) Previous methods have introduced algorithms which learn toize features in 3D-GS for enhanced scene understanding, but 3D-GS can fail without warning which presents a challenge for safety-critical robotic applications. 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.
• Score: 12.698075520631411
• License:
• Abstract: 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.

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