Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.

The status, challenges, and future of additive manufacturing in engineering

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

We provide a review of recent advances in 3D surface imaging technologies. We focus particularly on noncontact 3D surface measurement techniques based on structured illumination. The high-speed and high-resolution pattern projection capability offered by the digital light projection technology, together with the recent advances in imaging sensor technologies, may enable new generation systems for 3D surface measurement applications that will provide much better functionality and performance than existing ones in terms of speed, accuracy, resolution, modularization, and ease of use. Performance indexes of 3D imaging system are discussed, and various 3D surface imaging schemes are categorized, illustrated, and compared. Calibration techniques are also discussed, since they play critical roles in achieving the required precision. Numerous applications of 3D surface imaging technologies are discussed with several examples.

Structured-light 3D surface imaging: a tutorial

We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a disparity accuracy of 0.2 pixels on most observed surfaces, including in half-occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms.

/pdf/high-resolution-stereo-datasets-with-subpixel-accurate-kd3v44jdgt.pdf

High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

http://eia.udg.es/~qsalvi/papers/2010-PR.pdf

A state of the art in structured light patterns for surface profilometry

The purpose of this paper is to introduce and quickly make a reader familiar with OpenCV (Open Source Computer Vision) basics without having to go through the lengthy reference manuals and books. OpenCV is an open source library for image and video analysis, originally introduced more than decade ago by Intel. Since then, a number of programmers have contributed to the most recent library developments. The latest major change took place in 2009 (OpenCV 2) which includes main changes to the C++ interface. Nowadays the library has >2500 optimized algorithms. It is extensively used around the world, having >2.5M downloads and >40K people in the user group. Regardless of whether one is a novice C++ programmer or a professional software developer, unaware of OpenCV, the main library content should be interesting for the graduate students and researchers in image processing and computer vision areas. To master every library element it is necessary to consult many books available on the topic of OpenCV. However, reading such more comprehensive material should be easier after comprehending some basics about OpenCV from this paper.

/pdf/a-brief-introduction-to-opencv-2ipkar0ugh.pdf

A brief introduction to OpenCV

http://eia.udg.edu/~qsalvi/papers/2010-IVC.pdf

Efficient multiple phase shift patterns for dense 3D acquisition in structured light scanning

The understanding of body measurements and body shapes in and between populations is important and has many applications in medicine, surveying, the fashion industry, fitness, and entertainment. Body measurement using 3D surface scanning technologies is faster and more convenient than measurement with more traditional methods and at the same time provides much more data, which requires automatic processing. A multitude of 3D scanning methods and processing pipelines have been described in the literature, and the advent of deep learning-based processing methods has generated an increased interest in the topic. Also, over the last decade, larger public 3D human scanning datasets have been released. This paper gives a comprehensive survey of body measurement techniques, with an emphasis on 3D scanning technologies and automatic data processing pipelines. An introduction to the three most common 3D scanning technologies for body measurement, passive stereo, structured light, and time-of-flight, is provided, and their merits w.r.t. body measurement are discussed. Methods described in the literature are discussed within the newly proposed framework of five common processing stages: preparation, scanning, feature extraction, model fitting, and measurement extraction. Synthesizing the analyzed prior works, recommendations on specific 3D body scanning technologies and the accompanying processing pipelines for the most common applications are given. Finally, an overview of about 80 currently available 3D scanners manufactured by about 50 companies, as well as their taxonomy regarding several key characteristics, is provided in the Appendix.

/pdf/a-review-of-body-measurement-using-3d-scanning-2mome9rc83.pdf

A Review of Body Measurement Using 3D Scanning

3D scanning based on structured light (SL) has been proven to be a powerful tool to measure the three-dimensional shape of surfaces, especially in biomechanics. We define a set of conditions that an optimal SL strategy should fulfill in the case of static scenes and then we present an efficient solution based on improving the number-theoretic approach (NTA). The proposal is compared to the well-known Gray code (GC) plus phase shift (PS) technique and the original NTA, all satisfying the same set of conditions but obtaining significant improvements with our implementation. The technique is validated in biomechanical applications such as the scanning of a footprint left on a "foam box" typically made for that purpose, where one of the ultimate goals could be the production of a shoe insole.

/pdf/efficient-and-low-cost-3d-structured-light-system-based-on-a-4p1rwsldt5.pdf

Tomislav Pribanić

Papers

A state of the art in structured light patterns for surface profilometry

A brief introduction to OpenCV

Efficient multiple phase shift patterns for dense 3D acquisition in structured light scanning

A Review of Body Measurement Using 3D Scanning

Efficient and Low-Cost 3D Structured Light System Based on a Modified Number-Theoretic Approach