Desk3D Dataset: This webpage details the Desk3D dataset for depth-based object instance recognition. If you use this dataset please cite [1]. Overview: The dataset contains 6 object instances: Face, Ferrari, Kettle, Mini, Phone, Statue The test scenes are obtained by performing fusion [2] 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. Download files: 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 [3] to read from the raw oni files. Description of Files: Train.tar.gz: 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 [1], 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 [1] test frames where both tempExist and vld are 1 were used for testing of the given object. References: [1] Bonde, U., Badrinarayanan, V., & Cipolla, R. Robust Instance Recognition in Presence of Occlusion and Clutter. In ECCV 2014. [2] R. Rusu and S. Cousins. 3D is here: Point Cloud Library (PCL). In ICRA 2011 [3] http://uk.mathworks.com/matlabcentral/fileexchange/30242-kinect-matlab |