Hierarchical structure-and-motion recovery from uncalibrated images

TL;DR

This paper introduces Samantha, a hierarchical structure-and-motion recovery pipeline that improves scalability, stability, and autocalibration for uncalibrated images, demonstrated through experiments on real data.

Contribution

The paper presents a novel hierarchical approach to structure-and-motion recovery that reduces computational complexity and enables autocalibration without prior information.

Findings

Lower computational complexity compared to sequential methods

Enhanced stability and reduced drift in 3D reconstruction

Effective autocalibration from uncalibrated images

Abstract

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.