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The field of computer vision has been focusing on achieving accurate three-dimensional (3D) object representations from a single two-dimensional (2D) image through deep artificial neural networks. Recent advancements in 3D shape reconstruction techniques that combine structured light and deep learning show promise in acquiring high-quality geometric information about object surfaces. This paper introduces a new single-shot 3D shape reconstruction method that uses a nonlinear fringe transformation approach through both supervised and unsupervised learning networks. In this method, a deep learning network learns to convert a grayscale fringe input into multiple phase-shifted fringe outputs with different frequencies, which act as an intermediate result for the subsequent 3D reconstruction process using the structured-light fringe projection profilometry technique. Experiments have been conducted to validate the practicality and robustness of the proposed technique. The experimental results demonstrate that the unsupervised learning approach using a deep convolutional generative adversarial network (DCGAN) is superior to the supervised learning approach using UNet in image-to-image generation. The proposed technique's ability to accurately reconstruct 3D shapes of objects using only a single fringe image opens up vast opportunities for its application across diverse real-world scenarios.
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In recent years, integrating structured light with deep learning has gained considerable attention in three-dimensional (3D) shape reconstruction due to its high precision and suitability for dynamic applications. While previous techniques primarily focus on processing in the spatial domain, this paper proposes a novel time-distributed approach for temporal structured-light 3D shape reconstruction using deep learning. The proposed approach utilizes an autoencoder network and time-distributed wrapper to convert multiple temporal fringe patterns into their corresponding numerators and denominators of the arctangent functions. Fringe projection profilometry (FPP), a well-known temporal structured-light technique, is employed to prepare high-quality ground truth and depict the 3D reconstruction process. Our experimental findings show that the time-distributed 3D reconstruction technique achieves comparable outcomes with the dual-frequency dataset (p = 0.014) and higher accuracy than the triple-frequency dataset (p = 1.029 × 10-9), according to non-parametric statistical tests. Moreover, the proposed approach's straightforward implementation of a single training network for multiple converters makes it more practical for scientific research and industrial applications.
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Three-dimensional (3D) shape acquisition of objects from a single-shot image has been highly demanded by numerous applications in many fields, such as medical imaging, robotic navigation, virtual reality, and product in-line inspection. This paper presents a robust 3D shape reconstruction approach integrating a structured-light technique with a deep learning-based artificial neural network. The proposed approach employs a single-input dual-output network capable of transforming a single structured-light image into two intermediate outputs of multiple phase-shifted fringe patterns and a coarse phase map, through which the unwrapped true phase distributions containing the depth information of the imaging target can be accurately determined for subsequent 3D reconstruction process. A conventional fringe projection technique is employed to prepare the ground-truth training labels, and part of its classic algorithm is adopted to preserve the accuracy of the 3D reconstruction. Numerous experiments have been conducted to assess the proposed technique, and its robustness makes it a promising and much-needed tool for scientific research and engineering applications.
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Single-shot 3D shape reconstruction integrating structured light and deep learning has drawn considerable attention and achieved significant progress in recent years due to its wide-ranging applications in various fields. The prevailing deep-learning-based 3D reconstruction using structured light generally transforms a single fringe pattern to its corresponding depth map by an end-to-end artificial neural network. At present, it remains unclear which kind of structured-light patterns should be employed to obtain the best accuracy performance. To answer this fundamental and much-asked question, we conduct an experimental investigation of six representative structured-light patterns adopted for single-shot 2D-to-3D image conversion. The assessment results provide a valuable guideline for structured-light pattern selection in practice.
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Learning three-dimensional (3D) shape representation of an object from a single-shot image has been a prevailing topic in computer vision and deep learning over the past few years. Despite extensive adoption in dynamic applications, the measurement accuracy of the 3D shape acquisition from a single-shot image is still unsatisfactory due to a wide range of challenges. We present an accurate 3D shape acquisition method from a single-shot two-dimensional (2D) image using the integration of a structured-light technique and a deep learning approach. Instead of a direct 2D-to-3D transformation, a pattern-to-pattern network is trained to convert a single-color structured-light image to multiple dual-frequency phase-shifted fringe patterns for succeeding 3D shape reconstructions. Fringe projection profilometry, a prominent structured-light technique, is employed to produce high-quality ground-truth labels for training the network and to accomplish the 3D shape reconstruction after predicting the fringe patterns. A series of experiments has been conducted to demonstrate the practicality and potential of the proposed technique for scientific research and industrial applications.
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IMPORTANCE: In the past few decades, there has been rapid advancements in imaging technologies that have become irreplaceable in the pre-operative assessment of patients with pancreatic tumors. Modern imaging modalities, including computed tomography (CT) and endoscopic ultrasound (EUS), can provide critical information of the absence or presence of metastatic disease in pancreatic cancer, as well as details on the local extent and resectability, allowing for the selection of stage appropriate treatments and pre-operatively determined surgical approach. OBJECTIVE: The aim of this review is to discuss staging, resectability, and imaging for patients with pancreatic tumors. EVIDENCE REVIEW: A literature review was performed of articles relevant to the topics of staging, resectability, and imaging of pancreatic tumors. Imaging modalities included CT, EUS, magnetic resonance imaging (MRI), positron emission tomography (PET), antibody-based and narrow band imaging. FINDINGS: CT pancreas protocol combined with EUS serve as the primary modalities in diagnosis, staging, and surgical planning in patients with pancreatic tumors. MRI is an alternative to CT with near equivalent utility in the pre-operative setting. In some circumstances, PET-CT may be a cost-effective initial study to detect distant disease. CONCLUSIONS AND RELEVANCE: Current imaging technologies play a critical role in the evaluation of patients with pancreatic tumors. Advances in the past 3 decades in imaging technologies have revolutionized the process of assessment of stage and resectability in patients with pancreatic tumors. Future imaging technologies will address current limitation in the evaluation of occult metastatic disease.