Reconstruction and recognition of boundary representations from range images in SOMBRERO

The automatic segmentation of scattered 3D points into few, meaningful surfaces is a key step in range image processing, both in matching a scene with stored model objects and in reconstructing model objects from sensor data. Few segmentation algorithms exist for range images that are sufficiently robust, predictable, general, and efficiently parallelizable. The main contribution of the paper is a new split-and-merge approach with dynamic top-down triangulation that supports hierarchical resolution and backtracking. Feedback from model driven matching to data driven segmentation is provided in order to meet the recognition goal in a joint effort. Border control, quality criteria for surfaces and for partition points as well as unsupervised clustering help to achieve robustness and independence of image content. Results from a simple, high-noise range image are presented