Image-Based tracking of the teeth for orthodontic augmented reality
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Spin image revisited: fast candidate selection using outlier forest search
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Fast detection of multiple textureless 3-D objects
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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We present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust to small image transformations. This robustness is based on spread image gradient orientations and allows us to test only a small subset of all possible pixel locations when parsing the image, and to represent a 3D object with a limited set of templates. In addition, we demonstrate that if a dense depth sensor is available we can extend our approach for an even better performance also taking 3D surface normal orientations into account. We show how to take advantage of the architecture of modern computers to build an efficient but very discriminant representation of the input images that can be used to consider thousands of templates in real time. We demonstrate in many experiments on real data that our method is much faster and more robust with respect to background clutter than current state-of-the-art methods.