The stereo / flow benchmark consists of 194 training image pairs and 195 test image pairs, saved in loss less png format. Our evaluation server computes the average number of bad pixels for all non-occluded or occluded (=all groundtruth) pixels. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing disparity maps and flow fields.
Our evaluation table ranks all methods according to the number of non-occluded erroneous pixels at the specified disparity / end-point error threshold. All methods providing less than 100 % density have been interpolated using simple background interpolation as explained in the corresponding header file in the development kit. For each method we show:
Out-Noc: Percentage of erroneous pixels in non-occluded areas
Out-All: Percentage of erroneous pixels in total
Avg-Noc: Average disparity / end-point error in non-occluded areas
Avg-All: Average disparity / end-point error in total
Density: Percentage of pixels for which ground truth has been provided by the method
Note: Our main ranking is computed at 3 pixels error threshold, evaluating all pixels. For methods which do not provide dense result we use background interpolation to fill in missing values.
Stereo: Method uses left and right (stereo) images
HCI/Bosch Robust Vision Challenge: Optical flow and stereo vision challenge on high resolution imagery recorded at a high frame rate under diverse weather conditions (e.g., sunny, cloudy, rainy). The Robert Bosch AG provides a prize for the best performing method.
Image Sequence Analysis Test Site (EISATS): Synthetic image sequences with ground truth information provided by UoA and Daimler AG. Some of the images come with 3D range sensor information.
Middlebury Stereo Evaluation: The classic stereo evaluation benchmark, featuring four test images in version 2 of the benchmark, with very accurate ground truth from a structured light system. 38 image pairs are provided in total.
Daimler Stereo Dataset: Stereo bad weather highway scenes with partial ground truth for freespace
Make3D Range Image Data: Images with small-resolution ground truth used to learn and evaluate depth from single monocular images.
Stereo: Method uses left and right (stereo) images
Flow: Method uses optical flow (2 temporally adjacent images)
Multiview: Method uses more than 2 temporally adjacent images
Laser Points: Method uses point clouds from Velodyne laser scanner
GPS: Method uses GPS information
Motion stereo: Method uses epipolar geometry for computing optical flow