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IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis
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Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Superquadric-Based Object Recognition
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A Two Stage Neural Architecture for Segmentation and Superquadrics Recovery from Range Data
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Surface mesh segmentation and smooth surface extraction through region growing
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Efficient search and verification for function based classification from real range images
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Automatic extraction of surface structures in digital shape reconstruction
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Surface mesh segmentation and smooth surface extraction through region growing
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We present a novel approach to reliable and efficient recovery of part-descriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is based on the recover-and-select paradigm [10]. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise.