Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909377714192384 |
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| author | Wilson, Joey Almeida, Marcelino Sun, Min Mahajan, Sachit Ghaffari, Maani Ewen, Parker Ghasemalizadeh, Omid Kuo, Cheng-Hao Sen, Arnie |
| author_facet | Wilson, Joey Almeida, Marcelino Sun, Min Mahajan, Sachit Ghaffari, Maani Ewen, Parker Ghasemalizadeh, Omid Kuo, Cheng-Hao Sen, Arnie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02547 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting Wilson, Joey Almeida, Marcelino Sun, Min Mahajan, Sachit Ghaffari, Maani Ewen, Parker Ghasemalizadeh, Omid Kuo, Cheng-Hao Sen, Arnie Robotics Computer Vision and Pattern Recognition 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. |
| title | Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.02547 |