Trained 3D models for CNN based object recognition International Conference on Computer Vision Theory and Applications (VISAPP-17), February 27 – March 1, Porto, Portugal

Trained 3D models for CNN based object recognition International Conference on Computer Vision Theory and Applications (VISAPP-17), February 27 – March 1, Porto, Portugal

Conference Report

Abstract:

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.

Keywords:

Object Recognition, Fine-tuning CNNs, Domain fusion, Training on 3D data, Graphics assisted CNN