Bridging the Gap: A Real-World Dataset and Evaluation of Optical Flow Models in Large Displacement Scenarios
M. Timmerije (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jan Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
A.S. Gielisse – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Alexios Voulimeneas – Graduation committee member (TU Delft - Cyber Security)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Optical flow models excel on synthetic benchmarks but can struggle with real-world scenarios involving large displacements, which are critical for applications like autonomous navigation and augmented reality. To address this, we introduce a novel real-world dataset and evaluation framework, using a specialized annotation tool to capture ground truth optical flow in scenarios with fast movements and close-range objects. Our approach minimizes confounders, providing clear insights into model performance with large displacements. Findings show recent models outperform the previous state-of-the-art, RAFT, across all tested scenarios. Both the annotation tool and dataset are available to support further research.