4 papers
POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality
Joey Wilson, Marcelino Almeida, Sachit Mahajan +6
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…
These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models
Parker Ewen, Hao Chen, Seth Isaacson +3
This paper introduces a novel approach to uncertainty quantification for radiance fields by leveraging higher-order moments of the rendering equation. Uncertainty quantification is…
Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting
Joey Wilson, Marcelino Almeida, Min Sun +6
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 introd…
Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification
Parker Ewen, Gitesh Gunjal, Joey Wilson +3
Uncertainty in state or model parameters is common in robotics and typically handled by acquiring system measurements that yield information about the uncertain quantities of inter…