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A multi-body factorization method for motion analysis
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Articulated Structure from Motion by Factorization
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Motion Analysis of Articulated Objects from Monocular Images
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Learning kinematic models for articulated objects
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Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Rigid 3D geometry matching for grasping of known objects in cluttered scenes
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Learning temporally consistent rigidities
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
3D reconstruction of a smooth articulated trajectory from a monocular image sequence
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Automated articulated structure and 3D shape recovery from point correspondences
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We present an algorithm called Procrustes-Lo-RANSAC (PLR) to recover complete 3D models of articulated objects. Structure-from-motion techniques are used to capture 3D point cloud models of an object in two different configurations. Procrustes analysis, combined with a locally optimized RANSAC sampling strategy, facilitates a straightforward geometric approach to recovering the joint axes, as well as classifying them automatically as either revolute or prismatic. With the resulting articulated model, a robotic system is then able to manipulate the object along its joint axes at a specified grasp point in order to exercise its degrees of freedom. Because the models capture all sides of the object, they are occlusion-aware, meaning that the robot has knowledge of parts of the object that are not visible in the current view. Our algorithm does not require prior knowledge of the object, nor does it make any assumptions about the planarity of the object or scene. Experiments with a PUMA 500 robotic arm demonstrate the effectiveness of the approach on a variety of real-world objects containing both revolute and prismatic joints.