Geometric data abstraction using B-splines for range image segmentation

With the availability of cheap and powerful RGB-D sensors interest in 3D point cloud based methods has drastically increased. One common prerequisite of these methods is to abstract away from raw point cloud data, e.g. to planar patches, to reduce the amount of data and to handle noise and clutter. We present a novel method to abstract RGB-D sensor data to parametric surface models described by B-spline surfaces and associated boundaries. Data is first pre-segmented into smooth patches before B-spline surfaces are fitted. The best surface representations of these patches are selected in a merging procedure. Furthermore, we show how curve fitting estimates smooth boundaries and improves the given sensor information compared to hand-labelled ground truth annotation when using colour in addition to depth information. All parts of the framework are open-source
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and are evaluated on the object segmentation database (OSD) also available online, showing accuracy and usability of the proposed methods.