Probabilistic Inference on Manifolds and Its Applications in 3D Vision

Data Science Seminar

Tolga Birdal (Imperial College London)

Registration is required to access the Zoom webinar.

Abstract

Stochastic differential equations have lied at the heart of Bayesian inference even before being popularized by the recent diffusion models. Different discretizations corresponding to different MCMC implementations have been useful in sampling from non-convex posteriors. Through a series of papers, Tolga and friends have demonstrated that this family of methods are well applicable to the geometric problems arising in 3D computer vision. Before inviting the rest of this community to geometric diffusion models, Tolga will share his perspectives on two topics: (i) foundational tool of Riemannian MCMC methods for geometric inference and (ii) applications in probabilistic multi view pose estimation as well as inference of combinatorial entities such as correspondences. If time permits, Tolga will continue with his explorations in optimal-transport driven non-parametric methods for inference on Riemannian manifolds. Relevant papers include:

Bayesian Pose Graph Optimization [NeurIPS 2018]

Pobabilistic Permutation Synchronization [CVPR 2019 Honorable Mention]

Synchronizing Probability Measures on Rotations [CVPR 2020]