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<p><b>Abstract</b>—We present a method for segmenting and estimating the shape of 3D objects from range data. The technique uses model views, or aspects, to constrain the fitting of deformable models to range data. Based on an initial region segmentation of a range image, regions are grouped into aspects corresponding to the volumetric parts that make up an object. The qualitative segmentation of the range image into a set of volumetric parts not only captures the coarse shape of the parts, but qualitatively encodes the orientation of each part through its aspect. Knowledge of a part's coarse shape, its orientation, as well as the mapping between the faces in its aspect and the surfaces on the part provides strong constraints on the fitting of a deformable model (supporting both global and local deformations) to the data. Unlike previous work in physics-based deformable model recovery from range data, the technique does not require presegmented data. Furthermore, occlusion is handled at segmentation time and does not complicate the fitting process, as only 3D points known to belong to a part participate in the fitting of a model to the part. We present the approach in detail and apply it to the recovery of objects from range data.</p>
Shape recovery, range data, volumetric parts, model-based segmentation, object recognition, deformable superquadrics.
Dimitri Metaxas, Alex Pentland, Sven J. Dickinson, "The Role of Model-Based Segmentation in the Recovery of Volumetric Parts From Range Data", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 259-267, March 1997, doi:10.1109/34.584104
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