In this paper we address the problem in the offline stage of 3D modelling in feature based object recognition. While the online stage of recognition -feature matching and pose estimation, has been refined several times over the past decade incorporating filters and heuristics for robust and scalable recognition, the offline stage of creating feature based models remained unchanged. In this work we take advantage of the easily available 3D scanners and 3D model databases, and use them as our source of input for 3D CAD models of real objects. We process on the CAD models to produce feature-augmented trained models which can be used by any online recognition stage of object recognition. These trained models can also be directly used as a calibration rig for performing camera calibration from a single image. The evaluation shows that our fully automatically created feature-augmented trained models perform better in terms of recognition recall over the baseline -which is the tedious manual way of creating feature models. When used as a calibration rig, our feature augmented models achieve comparable accuracy with the popular camera-calibration techniques thereby making them an easy and quick way of performing camera calibration.
REF provided a new method for creating feature-augmented models in the presence of accurate 3D models at the training time.
Feature-augmented Trained Models for 6DOF Object Recognition and Camera Calibration
VISIGRAPP
Computer Science