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 tem- plate 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 rep- resent a 3D object with a limited set of templates. We show that together with a binary representation that makes eval- uation very fast and a branch-and-bound approach to effi- ciently scan the image, it can detect untextured objects in complex situations and provide their 3D pose in real-time.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Pascal Fua, Nassir Navab
• Keywords: CVPR 2010, Gradient Templates, Logical Optimization, Real Time Object Detection, SSE Optimization, Template Matching