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Technical University of Munich

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Datasets


3D Object in Clutter Recognition and Segmentation

This dataset focuses on the recognition of known objects in cluttered and incomplete 3D scans. It is composed of 150 synthetic scenes, captured with a (perspective) virtual camera, and each scene contains 3 to 5 objects. The model set is composed of 20 different objects, taken from different sources and then processed in order to obtain comparably smooth surfaces of almost uniform 100-350k triangles with an average resolution of 1.0.

You can download the collection of scenes, models, segmentation masks, and ground-truth rigid motions from here.

If you use the dataset, please cite the following paper:

A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes
E. Rodola, A. Albarelli, F. Bergamasco, and A. Torsello
International Journal of Computer Vision (IJCV), vol. 102, 2013

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