{'fraction_non_alphanumeric': 0.02332089552238806, 'fraction_numerical': 0.002798507462686567, 'mean_word_length': 5.6234567901234565, '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}}
Surface Saliency Detection Based on Curvature Co-Occurrence Histograms

This paper presents a 3-D surface saliency feature detection method, which can measure the importance geometry region of the point clouds. Different from existing approaches that are based on 3-D filter banks, the new method first constructs the curvature co-occurrence histogram (CCH), which encodes not only the global curvature occurrence, but also the co-occurrence of local distinctive features. And then, the mesh saliency is extracted from CCH through our mapping function. The proposed method is easy to implement and has high computation efficiency, which makes it especially suitable for large-scale 3-D point cloud preprocessing. The effectiveness of the saliency is demonstrated by point clouds’ registration and mesh simplification. Experimental results, from visual and quantification, demonstrate that our saliency contains more local geometrical details information and has more stable globally measurement comparing with curvature only described feature and center-surround saliency.