4 papers · 1 filter
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…
LatentBKI: Open-Dictionary Continuous Mapping in Visual-Language Latent Spaces with Quantifiable Uncertainty
Joey Wilson, Ruihan Xu, Yile Sun +4
This paper introduces a novel probabilistic mapping algorithm, LatentBKI, which enables open-vocabulary mapping with quantifiable uncertainty. Traditionally, semantic mapping algor…
MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty
Leo Bringer, Joey Wilson, Kira Barton +1
This paper introduces a Multi-modal Diffusion model for Motion Prediction (MDMP) that integrates and synchronizes skeletal data and textual descriptions of actions to generate refi…