Ruiming Yu;Hongshan Yu;Haiqiang Xu;Wei Sun;Naveed Akhtar;Yaonan Wang
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引用次数: 0
Abstract
Phase-shifting (PS) based structured light technology shows excellent 3D perception performance. However, it requires projecting a extensive array of patterns, imposing constraints on the measurement space, or embedding additional signals for phase unwrapping (PU), leading to motion artifacts and low robustness. To surmount these challenges, we propose a shadow-based, layer-by-layer phase unwrapping (SLBL-PU) method, which enables absolute phase recovery for deep objects without the need for any supplementary patterns. In the initial phase, attention is focused on a novel truncation feature within the local phase, facilitating the use of iterative PUs to derive the modulated phase. Inspired by shading theory, in the second phase, the absolute phase is restored based on the geometric relationship between the imaging system and the object shadows. Additionally, by incorporating a time-division multiplexing strategy, the efficiency of 3D reconstruction in dynamic scenes is further tripled. In experiments involving different depths, phase modulation, complex colored, and dynamic scenes, the proposed method demonstrated superior performance. Specifically, in static environments (0 mm/s), the proposed approach yields greater measurement accuracy (0.020 mm and 0.195 mm) than does the traditional spatial domain modulation (PS) method. In dynamic environments (15 mm/s), the proposed approach theoretically utilizes at least three patterns, with a defect rate lower than that of the nine-pattern, three-frequency PS method (8.58% and 14.68%).
期刊介绍:
The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.