A Probabilistic Graphical Model Approach to the Structure-and-Motion Problem

• URL: http://arxiv.org/abs/2110.03792v1
• Date: Thu, 7 Oct 2021 21:04:38 GMT
• Title: A Probabilistic Graphical Model Approach to the Structure-and-Motion Problem
• Authors: Simon Streicher, Willie Brink and Johan du Preez
• Abstract summary: We present a means of formulating and solving the well known structure-and-motion problem in computer vision. We model the unknown camera poses and 3D feature coordinates as well as the observed 2D projections as Gaussian random variables. We find that our approach shows promise in both simulation and on real-world data.
• Score: 2.2559617939136505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a means of formulating and solving the well known structure-and-motion problem in computer vision with probabilistic graphical models. We model the unknown camera poses and 3D feature coordinates as well as the observed 2D projections as Gaussian random variables, using sigma point parameterizations to effectively linearize the nonlinear relationships between these variables. Those variables involved in every projection are grouped into a cluster, and we connect the clusters in a cluster graph. Loopy belief propagation is performed over this graph, in an iterative re-initialization and estimation procedure, and we find that our approach shows promise in both simulation and on real-world data. The PGM is easily extendable to include additional parameters or constraints.

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