We demonstrate how to exploit reflections for accurate registration of shiny objects: The lighting environment can be retrieved from the reflections under a distant illumination assumption. Since it remains unchanged when the camera or the object of interest moves, this provides powerful additional constraints that can be incorporated into standard pose estimation algorithms. The key idea and main contribution of the paper is therefore to show that the registration should also be performed in the lighting environment space, instead of in the image space only. This lets us recover very accurate pose estimates because the specularities are very sensitive to pose changes. An interesting side result is an accurate estimate of the lighting environment. Furthermore, since the mapping from lighting environment to specularities has no analytical expression for objects represented as 3D meshes, and is not 1-to-1, registering lighting environments is far from trivial. However we propose a general and effective solution. Our approach is demonstrated on both synthetic and real images.

We propose a fully automated approach to calibrating multiple cameras whose fields of view may not all overlap. Our technique only requires waving an arbitrary textured planar pattern in front of the cameras, which is the only manual intervention that is required. The pattern is then automatically detected in the frames where it is visible and used to simultaneously recover geometric and photometric camera calibration parameters. In other words, even a novice user can use our system to extract all the information required to add virtual 3D objects into the scene and light them convincingly. This makes it ideal for Augmented Reality applications and we distribute the code under a GPL license.

In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our approach, mostly because it was simple to implement. It has proved effective but still too slow for real-time use. In this paper, we advocate instead the use of randomized trees as the classification technique. It is both fast enough for real-time performance and more robust. It also gives us a principled way not only to match keypoints but to select during a training phase those that are the most recognizable ones. This results in a real-time system able to detect and position in 3D planar, non-planar, and even deformable objects. It is robust to illuminations changes, scale changes and occlusions.