Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes

Authors:
Stefan Hinterstoisser;Vincent Lepetit;Slobodan Ilic;Stefan Holzer;Gary Bradski;Kurt Konolige;Nassir Navab
Affiliations:
CAMP, Technische Universität München (TUM), Germany;CV-Lab, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland;CAMP, Technische Universität München (TUM), Germany;CAMP, Technische Universität München (TUM), Germany;Industrial Perception, Palo Alto, CA;Industrial Perception, Palo Alto, CA;CAMP, Technische Universität München (TUM), Germany
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Year:
2012

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Abstract

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.