{'fraction_non_alphanumeric': 0.009845288326300985, 'fraction_numerical': 0.0014064697609001407, 'mean_word_length': 5.300884955752212, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spatial subdivision for piecewise planar object reconstruction

An algorithm is presented for piecewise planar segmentation of 3D point clouds, which uses spatial subdivision into finite volume elements. For each volume element a local plane is fitted and these planes are grouped to detect bigger planar structures and construct a piecewise planar object model. The algorithm has a higher detection sensitivity for small object planes than a previous global plane detection methods based on RANSAC fitting and plane sweeping. Experimental results are presented for a synthetic dataset, which was used to evaluate the algorithms performance and for a real dataset, which was used to compare it to other methods.