A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel. The dataset consists of 24 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20–50 frames, and has ground-truth disparity values, so that can be used for training deep learning-based methods. Each scene was rendered with a clean pass after modifying the production file of Sintel with reference to the MPI Sintel dataset.
Dataset of fluorescent mice brain vessels Confocal 3D volumes aligned to Light-Field images.
- Single volume dimension: 1287x1287x64.
- Number of samples: 362
- Voxel size: 0.086x0.086x0.9 um.
- Objective: 40x/1.3 Oil.
- Stain: tomato lectin (DyLight594 conjugated, DL-1177, Vector Laboratories).
Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth.