Simple domain adaptation for CAD based object recognition

Kripasindhu Sarkar, Didier Stricker

In: International Conference on Pattern Recognition Applications and Methods. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2019) February 19-21 Prague Czech Republic Scitepress 2019.


We present a simple method of domain adaptation between synthetic images and real images - by high quality rendering of the 3D models and correlation alignment. Using this method, we solve the problem of 3D object recognition in 2D images by fine-tuning existing pretrained CNN models for the object categories using the rendered images. Experimentally, we show that our rendering pipeline along with the correlation alignment improve the recognition accuracy of existing CNN based recognition trained on rendered images - by a canonical renderer - by a large margin. Using the same idea we present a general image classifier of common objects which is trained only on the 3D models from the publicly available databases, and show that a small number of training models are sufficient to capture different variations within and across the classes.


cnnsnap2.pdf (pdf, 7 MB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence