Stereo Matching Hand

A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes. Source: CATS: A Color and Thermal Stereo Benchmark

Middlebury 2005 is a stereo dataset of indoor scenes. Source: https://vision.middlebury.edu/stereo/data/scenes2005/ Image Source: https://vision.middlebury.edu/stereo/data/scenes2005/

iCubWorld datasets are collections of images recording the visual experience of iCub while observing objects in its typical environment, a laboratory or an office. The acquisition setting is devised to allow a natural human-robot interaction, where a teacher verbally provides the label of the object of interest and shows it to the robot, by holding it in the hand; the iCub can either track the object while the teacher moves it, or take it in its hand.

The Middlebury 2006 is a stereo dataset of indoor scenes with multiple handcrafted layouts. Source: https://vision.middlebury.edu/stereo/data/scenes2006/ Image Source: https://vision.middlebury.edu/stereo/data/scenes2006/

The Middlebury 2001 is a stereo dataset of indoor scenes with multiple handcrafted layouts. Source: https://vision.middlebury.edu/stereo/data/scenes2001/ Image Source: https://vision.middlebury.edu/stereo/data/scenes2001/

The dataset consists of 1024 x 1024 bitmap (.bmp) images, each containing a 16 x 16 array of image patches. Each patch is sampled as 64x64 grayscale, with a canonical scale and orientation. For details of how the scale and orientation is established, please see the paper. Two associated metadata files are included. The first file "info.txt" contains the match information. Each row of info.txt corresponds corresponds to a separate patch, with the patches ordered from left to right and top to bottom in each bitmap image. The first number on each row of info.txt is the 3D point ID from which that patch was sampled -- patches with the same 3D point ID are projected from the same 3D point (into different images). The second number in is not used at present. The file "interest.txt" has information about the original interest points. Each row of interest.txt also corresponds to a separate patch, so it has the same number of rows as info.txt. The first number is the ID of the reference image in which the interest point was found. IMPORTANT: in order to establish matches and non-matches, you must use patches with the same reference image ID. Correspondences were found by projecting between images using this reference image only, so it is possible that patches with different 3D point ID's that have different reference image ID's could actually correspond to the same 3D point. The other information in interest.txt is: x, y, orientation, scale (log2 units). In order to make sure that non-matches were sufficiently different, we checked that these values were sufficiently far apart when establishing non-matches. To allow researchers to replicate our learning results (if desired), we have include the match files that we used to generate the results in the paper. These are name "m50_n1_n2.txt" where n1 and n2 are the number of matches and non-matches present in the file. The format of the file is as follows: patchID1 3DpointID1 unused1 patchID2 3DpointID2 unused2 ... "matches" have the same 3DpointID, and correspond to interest points that were detected with 5 pixels in position, and agreeing to 0.25 octaves of scale and pi/8 radians in angle. "non-matches" have different 3DpointID's, and correspond to interest points lying outside a range of 10 pixels in position, 0.5 octaves of scale and pi/4 radians in angle.