Rolling Shutter Pose and Ego-motion Estimation using Shape-from-Template

Yizhen Lao, Omar Ait-Aider, Adrien Bartoli; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 466-482

We propose a new method for the absolute camera pose problem (PnP) which handles Rolling Shutter (RS) effects. Unlike all existing methods which perform 3D-2D registration after augmenting the Global Shutter (GS) projection model with the velocity parameters under various kinematic models, we propose to use local differential constraints. These are established by drawing an analogy with Shape-from-Template (SfT). The main idea consists in considering that RS distortions due to camera ego-motion during image acquisition can be interpreted as virtual deformations of a template captured by a GS camera. Once the virtual deformations have been recovered using SfT, the camera pose and ego-motion are computed by registering the deformed scene on the original template. This 3D-3D registration involves a 3D cost function based on the Euclidean point distance, more physically meaningful than the re-projection error or the algebraic distance based cost functions used in previous work. Results on both synthetic and real data show that the proposed method outperforms existing RS pose estimation techniques in terms of accuracy and stability of performance in various configurations.