Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes - Hinterstoisser, Stefan; Lepetit, Vincent; Ilic, Slobodan; Holzer, Stefan; Bradski, Gary; Konolige, Kurt; Navab, Nassir (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013)
We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13% with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods. © 2013 Springer-Verlag.
CRIS-Team Scopus Importer:2023-03-08