Towards Rotation-only Imaging Geometry: Rotation Estimation
This work addresses the need for more accurate and efficient 3D visual computing in computer vision, representing an incremental advancement by building on pose-only imaging geometry.
This paper tackles the problem of improving Structure from Motion (SfM) by proposing a rotation-only optimization framework that expresses translation in terms of rotation, condensing imaging geometry onto the rotation manifold. The results demonstrate superior accuracy and robustness over state-of-the-art rotation estimation methods, comparable to multiple bundle adjustment iterations.
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.