This webpage details the Desk3D dataset for depth-based object instance recognition. If you use this dataset please cite .
The dataset contains 6 object instances: Face, Ferrari, Kettle, Mini, Phone, Statue
The test scenes are obtained by performing fusion  on 5 consecutive frames. Based on the testing challenge we divided the test scenes into 4 main scenarios:
Test scenario 1:
Test scenario 2:
Test scenario 4:
Further we also have a test scene with only background to test the number of false positives and determine the classifiers separability while plotting precision-recall curves.
There are 7 main download files compressed with tar.gz:
readMe.txt: A download version of this text.
train.tar.gz: Contains the training models of each instance along with training sequences of random background.
testScenario1.tar.gz: Contains the test scenes of test scenario 1.
testScenario2.tar.gz: Contains the test scenes of test scenario 2.
testScenario3.tar.gz: Contains the test scenes of test scenario 3.
testScenario4.tar.gz: Contains the test scenes of test scenario 4.
testClutter.tar.gz: Contains test scenes with only background clutter.
In addition, we also have a "codes.tar.gz" file that contains *.m files to read the data and a oni.tar.gz file that contains all the video sequence captured using a kinect which contains both the RGB and depth fields. The oni.tar.gz is the largest file (~8GB) and will be made available only on request. You'll need  to read from the raw oni files.
Description of Files:
For each object we have 8 different scans of the object saved as pose*/cloud_mesh_"object".ply.
Each scan is accompanied with a "tfrom.aln" file which can be used to transform them into their canonical pose.
The ground-truth of test scenes is with this canonical pose.
In addition we also give a mesh model saved as "mesh.ply" for each object.
Their are 3 background training sequences: scene_clutter1 (100 frames), scne_clutter2 (50 frames) and scene_clutter3 (100 frames).
In , scene_clutter1 & 3 were used for training and 2 for validation.
TestScenario1-4 and testClutter.tar.gz:
For each test scene, the fused frames are save as fusion/cloud_*ply.
The ground truth for each object is saved as dataIcp_"object"_check.mat.
These files contain three main fields:
In  test frames where both tempExist and vld are 1 were used for testing of the given object.
 Bonde, U., Badrinarayanan, V., & Cipolla, R. Robust Instance Recognition in Presence of Occlusion and Clutter. In ECCV 2014.
 R. Rusu and S. Cousins. 3D is here: Point Cloud Library (PCL). In ICRA 2011