Motion Estimation for Large Displacements and Deformations

The preprint of our paper Motion Estimation for Large Displacements and Deformations -published in Scientific Reports- is available on arxiv.org. The work is co-authored by Qiao Chen and Charalambos Poullis. The camera-ready paper is now available: https://www.nature.com/articles/s41598-022-21987-7

The work was featured in LeDevoir (article).

TL;DR: Large-scale images such as wide-area motion imagery cannot be handled by deep learning architectures. The standard procedural method for image-based reconstruction is Structure-from-Motion followed by Multiview Stereo which is time-consuming and can result in incomplete and disjoint reconstructions. In this paper, we propose a variational optical flow method based on graph and feature matching tailored for the dense matching and reconstruction of large-scale images. The proposed method surpasses all variational methods and gives comparable results with the latest deep learning approaches.