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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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