10 October 1994 Model-based 3D object recognition by a hybrid hypothesis generation and verification approach
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We present a model-based 3D object recognition architecture that combines pose estimation derived from range images and hypothesis verification derived from intensity images. The architecture takes advantage of the geometrical nature of range images for generating a number of hypothetical poses of objects. Pose and object models are then used to reconstruct a synthetic view of the scene to be compared to the real intensity image for verification. According to the architecture a system has been implemented and successful experiments have been performed with boxes of different shapes and textures. Recognition with our approach is precise and robust. In particular verification can detect false poses resulting from wrong groupings. In addition, the system provides the interesting features to recognize the true pose of shape-symmetrical objects and also to recognize objects that are ambiguous from their sole shape.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Philippe Gingins, Philippe Gingins, Heinz Huegli, Heinz Huegli, } "Model-based 3D object recognition by a hybrid hypothesis generation and verification approach", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188936; https://doi.org/10.1117/12.188936


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