The eTrims dataset is comprised of two datasets, the 4-Class eTRIMS Dataset with 4 annotated object classes and the 8-Class eTRIMS Dataset with 8 annota...
urban, semantic segmentation, procedural reconstructionZurich City Hall dataset (also CIPA dataset) nformation: Place: City Hall, Zurich, Switzerland Number of Images: 15, 1280 x 1000 pixels Camera: Fuj...
urban, 3d reconstruction, photogrammetry, sfm, zurichZurich Hoengg (Switzerland) is an aerial dataset. The dataset consists of 4 aerial images in colour (Figures 2-5), scanned with 14 microns, the forma...
semantic segmentation, aerial, outdoorThe multi-scale Weizmann horses (originally from Eran Borenstein, adapted by Jamie Shotton) consists of 656 images which is split into 50+50training, 50...
object segmentation, clutter, horse, object detection by shape, natureThe Hopkins 155 Dataset has been created with the goal of providing an extensive benchmark for testing feature based motion segmentation algorithms. It ...
urban, optical flow, stereo estimation, motion segmentationThe goal of LabelMe is to provide an online annotation tool to build image databases for computer vision research. You can contribute to the database by...
urban, semantic segmentation, software, semantic, outdoor, object detectionThe Caltech Pedestrian Dataset consists of approximately 10 hours of 640x480 30Hz video taken from a vehicle driving through regular traffic in an urban...
urban, pedestrian, object detectionDaimler Stereo Pedestrian Detection Benchmark C. Keller, M. Enzweiler, and D. M. Gavrila, A New Benchmark for Stereo-based Pedestrian Detection, Proc...
urban, pedestrian, object detectionDaimler Multi-Cue, Occluded Pedestrian Classification Benchmark Training and test samples have a resolution of 48 x 96 pixels with a 12-pixel border a...
image classification, urban, pedestrian, object detectionThe ETHZ Shape classes dataset from Vittorio Ferrari [?] consists of five object classes and a total of 255 images. All classes contain significant intr...
clutter, swan, bottle, matching, nature, object detection by shape, mug, giraffe, segmentation, applelogo