Towards Rotation-only Imaging Geometry: Rotation Estimation

TL;DR

This paper introduces a rotation-only optimization framework for Structure from Motion that leverages the relationship between scene structure, rotation, and translation, leading to improved accuracy and robustness in 3D reconstruction.

Contribution

It proposes a novel rotation-only approach that expresses translation in terms of rotation, simplifying the imaging geometry and enhancing estimation performance.

Findings

Superior accuracy over existing rotation estimation methods

Robustness comparable to multiple bundle adjustment iterations

Effective in both two-view and multi-view scenarios

Abstract

Structure from Motion (SfM) is a critical task in computer vision, aiming to recover the 3D scene structure and camera motion from a sequence of 2D images. The recent pose-only imaging geometry decouples 3D coordinates from camera poses and demonstrates significantly better SfM performance through pose adjustment. Continuing the pose-only perspective, this paper explores the critical relationship between the scene structures, rotation and translation. Notably, the translation can be expressed in terms of rotation, allowing us to condense the imaging geometry representation onto the rotation manifold. A rotation-only optimization framework based on reprojection error is proposed for both two-view and multi-view scenarios. The experiment results demonstrate superior accuracy and robustness performance over the current state-of-the-art rotation estimation methods, even comparable to multiple bundle adjustment iteration results. Hopefully, this work contributes to even more accurate, efficient and reliable 3D visual computing.