Learning to Solve Hard Minimal Problems

School of Mathematics Colloquium
Thursday, October 13, 2022 - 11:00am for 1 hour (actually 50 minutes)
Skiles 006
Anton Leykin – Georgia Tech – leykin@math.gatech.edu
Gong Chen, Ben Jaye, Tom Kelly

The main result in this talk concerns a new fast algorithm to solve a minimal problem with many spurious solutions that arises as a relaxation of a geometric optimization problem. The algorithm recovers relative camera pose from points and lines in multiple views. Solvers like this are the backbone of structure-from-motion techniques that estimate 3D structures from 2D image sequences.  

Our methodology is general and applicable in areas other than computer vision. The ingredients come from algebra, geometry, numerical methods, and applied statistics. Our fast implementation relies on a homotopy continuation optimized for our setting and a machine-learned neural network.

(This covers joint works with Tim Duff, Ricardo Fabbri, Petr Hruby, Kathlen Kohn, Tomas Pajdla, and others.

The talk is suitable for both professors and students.)