Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning.

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.

Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning.

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.

Single-shot 3D shape acquisition using a learning-based structured-light technique.

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.

Different structured-light patterns in single-shot 2D-to-3D image conversion using deep learning.

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.

Use of imaging as staging and surgical planning for pancreatic surgery.

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.