{'fraction_non_alphanumeric': 0.01368421052631579, 'fraction_numerical': 0.0010526315789473684, 'mean_word_length': 5.135483870967742, '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}}
Adaptive multi-resolution fitting and its application to realistic head modeling

The general approach for object modeling is to construct the surface from the high-quality range points obtained from laser scanners. In this paper, we face the noise point cloud obtained from image sequences by a common camera and develop a novel algorithm of adaptive multi-resolution fitting (AMRF) for object modeling. This algorithm combines the adaptive subdivision scheme with multi-resolution fitting so that the control model is subdivided locally and adaptively according to the local complexity of the point cloud and approximates the 3D data level by level. The proposed method can conquer the holes and outliers efficiently and create full compatibility between the complexity of the mesh model and the representation of the local details. We apply the proposed method to the complete head modeling with the real data, and the results seem very promising.