Dominant orientation templates for real-time detection of texture-less objects

We present a method for real-time 3D object detection that does not require a time consuming training stage, and can handle untextured objects. At its core, is a novel template representation that is designed to be robust to small image transformations. This robustness based on dominant gradient orientations lets us 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. We show that together with a binary representation that makes evaluation very fast and a branch-and-bound approach to efficiently scan the image, it can detect untextured objects in complex situations and provide their 3D pose in real-time.

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