Real-time learning and detection of 3D texture-less objects: A scalable approach

Abstract

We present a method for the learning and detection of multiple rigid texture-less 3D objects intended to operate at frame rate speeds for video input. The method is geared for fast and scalable learning and detection by combining tractable extraction of edgelet constellations with library lookup based on rotation- And scale-invariant descriptors. The approach learns object views in real-time, and is generative - enabling more objects to be learnt without the need for re-training. During testing, a random sample of edgelet constellations is tested for the presence of known objects. We perform testing of single and multi-object detection on a 30 objects dataset showing detections of any of them within milliseconds from the object's visibility. The results show the scalability of the approach and its framerate performance.