title: Gradient response maps for real-time detection of textureless objects.
abstract: 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.
authors.affiliation: Department of Computer Aided Medical Procedures (CAMP), Technische Universität München, Garching bei München 85478, Germany. hinterst@in.tum.de
authors.forename: Stefan
authors.initials: S
authors.lastname: Hinterstoisser
authors.forename: Cedric
authors.initials: C
authors.lastname: Cagniart
authors.forename: Slobodan
authors.initials: S
authors.lastname: Ilic
authors.forename: Peter
authors.initials: P
authors.lastname: Sturm
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
authors.forename: Pascal
authors.initials: P
authors.lastname: Fua
authors.forename: Vincent
authors.initials: V
authors.lastname: Lepetit
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D015203:Reproducibility of Results; D013499:Surface Properties