A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery

02/10/2017
by   Soumyadip Sengupta, et al.
0

Accurate estimation of camera matrices is an important step in structure from motion algorithms. In this paper we introduce a novel rank constraint on collections of fundamental matrices in multi-view settings. We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6. Moreover, this matrix forms the symmetric part of a rank 3 matrix whose factors relate directly to the corresponding camera matrices. We use this new characterization to produce better estimations of fundamental matrices by optimizing an L1-cost function using Iterative Re-weighted Least Squares and Alternate Direction Method of Multiplier. We further show that this procedure can improve the recovery of camera locations, particularly in multi-view settings in which fewer images are available.

READ FULL TEXT
research
12/02/2018

GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices

This paper addresses the problem of recovering projective camera matrice...
research
12/22/2014

A New Way to Factorize Linear Cameras

The implementation details of factorizing the 3x4 projection matrices of...
research
12/05/2017

Optimal Sample Complexity for Stable Matrix Recovery

Tremendous efforts have been made to study the theoretical and algorithm...
research
01/27/2019

Resultant Based Incremental Recovery of Camera Pose from Pairwise Matches

Incremental (online) structure from motion pipelines seek to recover the...
research
11/30/2019

Averaging Essential and Fundamental Matrices in Collinear Camera Settings

Global methods to Structure from Motion have gained popularity in recent...
research
08/08/2018

On the Solvability of Viewing Graphs

A set of fundamental matrices relating pairs of cameras in some configur...
research
01/24/2022

What is the cost of adding a constraint in linear least squares?

Although the theory of constrained least squares (CLS) estimation is wel...

Please sign up or login with your details

Forgot password? Click here to reset