Viewpoint simulation for camera pose estimation from an unstructured scene model

Pierre Rolin (1) , Marie-Odile Berger (1) , Frédéric Sur (1)

1 MAGRIT - Visual Augmentation of Complex Environments

We consider the problem of camera pose estimation from a scene model obtained beforehand by a structure-from-motion (SfM) algorithm. The model is made of 3D points, each one of them being represented by its coordinates and a set of photometric descriptors such as SIFT, extracted from some of the input images of the SfM stage. Pose estimation is based on the matching of interest points from a test view with model points, using the descriptors. Descriptors having a limited invariance with respect to viewpoint changes, such an approach is likely to fail when the test view is far away from the images used to construct the model. Viewpoint simulation techniques, as ASIFT, have proved effective for wide-baseline image matching. This paper explores how these techniques can enrich a scene model by adding descriptors from simulated views, using either orthographic or pinhole virtual cameras. Viewpoint simulation permits pose estimation in situations where the approach based on the sole SIFT descriptors simply fails.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Pierre Rolin, Marie-Odile Berger, Frédéric Sur. Viewpoint simulation for camera pose estimation from an unstructured scene model. International Conference on Robotics and Automation, May 2015, Seattle, United States. ⟨hal-01166785⟩