{'fraction_non_alphanumeric': 0.014856081708449397, 'fraction_numerical': 0.0009285051067780873, 'mean_word_length': 5.493975903614458, '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}}
Hierarchical Point Simplification Using Coplanar Criterion

This investigation presents a novel rapid and effective point simplification algorithm utilizing point cloud without normals. Local coplanar analysis is utilized to obtain the relevant points from a point set sampled from 3D objects. The local coplanar analysis, on the basis of an octree data structure with an inner point distribution of a cube, can determine whether these points are coplanar. The relevant points, called the base model, were reconstructed to triangular mesh. In addition to the successful reconstruction, the error rate of the base model within a specific tolerance level. By using the octree data structure, this study proposes some hierarchical rendering for the base model to suit user demand and produce a uniform or feature-sensitive simplified model that facilitates rapid further mesh-based applications. Finally, output of the proposed method is a hierarchical triangular mesh that inherently supports generation of multi-resolution representations for the applications of level of detail