Variational Disparity Estimation Framework for Plenoptic Image
This work addresses the challenge of precise 3D reconstruction from light-field images, which is important for applications like computational photography and computer vision, though it appears incremental as it builds on existing variational methods with specific enhancements.
The paper tackles the problem of accurately estimating disparity maps from plenoptic images, achieving sub-pixel precision through a variational framework with a light-field motion tensor, warping for large displacements, and enhanced occlusion handling, demonstrating excellent performance compared to Lytro software and contemporary approaches on synthetic and real-world datasets.
This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping strategy is embedded in our framework for tackling the large displacement problem. We also show that by applying a simple regularization term and a guided median filtering, the accuracy of displacement field at occluded area could be greatly enhanced. We demonstrate the excellent performance of the proposed framework by intensive comparisons with the Lytro software and contemporary approaches on both synthetic and real-world datasets.