Trained 3D Models for CNN based Object Recognition

Kripasindhu Sarkar, Kiran Varanasi, Didier Stricker

In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017). International Conference on Computer Vision Theory and Applications (VISAPP-17) Seiten 130-137 5 ISBN 978-989-758-226-4 SciTePress 2017.


We present a method for 3D object recognition in 2D images which uses 3D models as the only source of the training data. Our method is particularly useful when a 3D CAD object or a scan needs to be identified in a catalogue form a given query image; where we significantly cut down the overhead of manual labeling. We take virtual snapshots of the available 3D models by a computer graphics pipeline and fine-tune existing pretrained CNN models for our object categories. Experiments show that our method performs better than the existing local-feature based recognition system in terms of recognition recall.

snap_cnn_cameraready_short.pdf (pdf, 4 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence