{'fraction_non_alphanumeric': 0.013658536585365854, 'fraction_numerical': 0.000975609756097561, 'mean_word_length': 4.965116279069767, '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}}
Volumetric Reconstruction of Objects and Scenes Using Range Images

Abstract This paper reviews volumetric methods for fusing sets of range images to create 3D models of objects or scenes. It also presents a new reconstruction method, which is a hybrid that combines several desirable aspects of techniques discussed in the literature. The proposed reconstruction method projects each point, or voxel, within a volumetric grid back onto a collection of range images. Each voxel value represents the degree of certainty that the point is inside the sensed object. The certainty value is a function of the distance from the grid point to the range image, as well as the sensor's noise characteristics. The super-Bayesian combination formula is used to fuse the data created from the individual range images into an overall volumetric grid. We obtain the object model by extracting an isosurface from the volumetric data using a version of the marching cubes algorithm. Results are shown from simulations and real range finders.