DDFF 12-Scene dataset consists of 720 lightfield images and coregistered depth maps.
- Lightfield: 4D lightfield images; each of which has 9 × 9 × 383 × 552 undistorted subapertures Images are saved as numpy arrays and can be loaded as follows:
import numpy as np lf = np.load('LF_0001.npy') - Lightfield-mat: 4D lightfield images in Matlab format
- Depth: registered depth maps; recorded in meters and scaled by a factor of 1000. Depth images are saved in uint16 bits and only available for "train" and "val" sets:
import cv2 from PIL import Image # in meters depth = cv2.imread('DEPTH_0001.png', cv2.IMREAD_ANYDEPTH) * 0.001 depth = np.array(Image.open('DEPTH_0001.png'), dtype=np.float) * 0.001 - RawImage: raw images consist of Lytro ILLUM RAW formatted images
- CalibPattern: calibration pattern for the Lytro ILLUM camera
- WhiteImages: white images required by the Lytro Desktop
Depth From Focus Competition
Please submit your test results to the DFF competition.
License
All data in the DDFF 12-Scene benchmark is licensed under a Creative Commons 4.0 Attribution License (CC BY 4.0).
Log
[24-04-2018] – Trainval/Test hdf5 files added
[07-12-2017] – Lighfield images in Matlab format
[05-12-2017] – Lighfield calibration pattern and white images
[15-09-2017] – Lightfield images, registered depth maps and raw Lytro ILLUM images
Bibtex
@InProceedings{hazirbas18ddff,
author = {C. Hazirbas and S. G. Soyer and M. C. Staab and L. Leal-Taixé and D. Cremers},
title = {Deep Depth From Focus},
booktitle = {Asian Conference on Computer Vision (ACCV)},
year = {2018},
month = {December},
eprint = {1704.01085},
url = {https://hazirbas.com/projects/ddff/},
}