Space and camera path reconstruction for omni-directional vision

TL;DR

This paper presents a method for reconstructing scenes and camera paths from omni-directional images, providing conditions for uniqueness and robust solutions applicable to dynamic environments and various practical applications.

Contribution

It establishes sharp conditions for unique scene and camera motion reconstruction from omni-directional images and introduces a least squares approach for large, over-determined systems.

Findings

Unique reconstruction with three points and cameras in general position

Robust least squares solution for large systems

Applicability to dynamic environments and diverse fields

Abstract

In this paper, we address the inverse problem of reconstructing a scene as well as the camera motion from the image sequence taken by an omni-directional camera. Our structure from motion results give sharp conditions under which the reconstruction is unique. For example, if there are three points in general position and three omni-directional cameras in general position, a unique reconstruction is possible up to a similarity. We then look at the reconstruction problem with m cameras and n points, where n and m can be large and the over-determined system is solved by least square methods. The reconstruction is robust and generalizes to the case of a dynamic environment where landmarks can move during the movie capture. Possible applications of the result are computer assisted scene reconstruction, 3D scanning, autonomous robot navigation, medical tomography and city reconstructions.