Semantic Segmentation Dataset
This page provides a RGB-D Segmentation dataset with groundtruth acquired with a Microsoft Kinect device. The dataset includes 5 (+ 1 for the background) categories of common grocery products such as packets of biscuits, juice bottles, coffee cans and boxes of salt, of different brands and colors. The training set includes 3 model views for each category, while the testing scenes are 16, including a high degree of clutter and occlusions. Thanks to the deployed device, this dataset includes both color and depth. It also includes ground-truth, i.e. the correct label to be assigned to each point of the test set.
The dataset has been proposed in . Please cite appropriately this page and  if you plan to use the dataset for any kind of scientific work or publication. For any question feel free to write at:
federico (DOT) tombari (AT) unibo (DOT) it.
The dataset is structured into two main folders:
Each model of each of the 6 categories includes 3 files:
Each of the 16 test scenes includes the following files (XX is a number from 0 to 15):
The dataset can be downloaded as two separate zip files (training set and test set):
Dataset (training set) (zip format - 43.7 MB)
Dataset (test set) (zip format - 310 MB)
The Figure shows an example of a scene of the dataset, reporting the RGB image, a snapshot of the 3D mesh and a snapshot of the 3D groundtruth labels.
 F. Tombari, L. Di Stefano, S. Giardino, "Online Learning for Automatic Segmentation of 3D Data", IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS '11), 2011