{'fraction_non_alphanumeric': 0.023529411764705882, 'fraction_numerical': 0.0022624434389140274, 'mean_word_length': 5.659638554216867, '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}}
Accurate 3D Reconstruction of Dynamic Objects by Spatial-Temporal Multiplexing and Motion-Induced Error Elimination

Three-dimensional (3D) reconstruction of dynamic objects has broad applications, including object recognition and robotic manipulation. However, achieving high-accuracy reconstruction and robustness to motion simultaneously is a challenging task. In this paper, we present a novel method for 3D reconstruction of dynamic objectS, whose main features are as follows. Firstly, a structured-light multiplexing method is developed that only requires 3 patterns to achieve high-accuracy encoding. Fewer projected patterns require shorter image acquisition time, thus, the object motion is reduced in each reconstruction cycle. The three patterns, i.e. spatial-temporally encoded patterns, are generated by embedding a specifically designed spatial-coded texture map into the temporal-encoded three-step phase-shifting fringes. A temporal codeword and three spatial codewords are extracted from the composite patterns using a proposed extraction algorithm. The two types of codewords are utilized separately in stereo matching: the temporal codeword ensures the high accuracy, while the spatial codewords are responsible for removing phase ambiguity. Secondly, we aim to eliminate the reconstruction error induced by motion between frames abbreviated as motion induced error (MiE). Instead of assuming the object to be static when acquiring the 3 images, we derive the motion of projection pixels among frames. Using the extracted spatial codewords, correspondences between different frames are found, i.e. pixels with the same codewords are traceable in the image sequences. Therefore, we can obtain the phase map at each image-acquisition moment without being affected by the object motion. Then the object surfaces corresponding to all the images can be recovered. Experimental results validate the high reconstruction accuracy and precision of the proposed method for dynamic objects with different motion speeds. Comparative experiments show that the presented method demonstrates superior performance with various types of motion, including translation in different directions and deformation.