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The construction of a surface model from range data may be undertaken at any point in a continuum of scales that reflects the level of detail of the resulting model. This continuum relates the construction parameters to the scale of the model. We propose methods to dynamically reprocess range data at different scales. The construction result from a single scale is automatically evaluated, causing reconstruction at a different scale when user-defined criteria are not met. We demonstrate our methods in constructing a planar b-rep space envelope (a scene representation) for over 400 range images. The experiments demonstrate the ability to construct 100 percent valid models, with the scale of detail within specified requirements.

A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.

A new approach is presented for extracting an explicit 3D shape model from a single range image. One novel aspect is that the model represents both observed object surfaces, and surfaces which bound the volume of occluded space. Another novel aspect is that the approach does not require that the range image segmentation be perfect. The low-level segmentation may be such that the model-building process encounters topology versus geometry conflicts. The model-building process is designed to be "fail soft" in the face of such problems. The portion of the 3D model where a problem presents itself is "glued" together in a manner meant to minimize the disturbance in the 3D shape. The goal is to produce a valid boundary-representation which can be processed by higher-level routines. A third novel aspect of this work is that the implementation has been evaluated on over 200 real range images of polyhedral objects, with no operator intervention and all parameters held constant, and obtained a 97% success rate in creating valid b-reps. >