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Retailing thought and practice is premised on the assumption that consumers visit retailers to search for and acquire objects produced by manufacturers. In essence, we assume that the acts of consuming and producing are conducted by separate entities. This unspoken yet familiar premise shapes the questions retail scholars ask and the way retail practitioners think about their industry. Although this assumption accurately depicted retailing since the Industrial Revolution, its relevance is being challenged by a growing set of individuals who are equipped with new digital tools to engage in self-manufacturing. In this chapter, we examine self-manufacturing with a particular focus on the recent rise of desktop 3D printing. After discussing this new technology and reviewing the literature, we offer a conceptual classification of four distinct types of 3D printed objects and use this classification to inform a content analysis of over 400 of these objects. Based on this review and analysis, we discuss the implications of self-manufacturing for retailing thought and practice.

In model‐based recognition the 3D models of objects are stored in a model library during an offline phase. During the online recognition phase, a view of the scene is matched with the model library to identify the location and pose of certain library objects in the scene. Aims to focus on the process of 3D modeling and model‐based recognition.

This paper discusses the process of 3D modeling and model‐based recognition along with their potential applications in industry with a particular emphasis on robot grasp analysis. The paper also emphasises the main challenges in these areas and give a brief literature review.

In order to develop an automatic 3D model‐based object recognition system it is necessary to automate the process of 3D modeling and recognition. The challenge in automating the 3D modeling process is to develop an automatic correspondence technique. The core of recognition is the representation scheme. Recognition is an online process. Therefore, representation and matching must be very fast in order to facilitate real time recognition.

There are numerous applications of 3D modeling in a variety of areas ranging from the entertainment industry to industrial automation. Some of its applications include computer graphics, virtual reality, medical imaging, reverse engineering, and 3D terrain construction.

Provides information on 3D modeling which constitutes an important part of computer vision or robot vision.

This paper aims to help archaeologists, museums’ curators and technicians in understanding the principle of using the photogrammetry and 3D scanner for the museum archaeological objects in a practical way by presenting specific examples for both methods. Another purpose is to evaluate the performance offered by the photogrammetry and the three-dimensional (3D) scanner device, with the aim of providing a suitable solution to the different shapes and sizes of the archaeological objects.

The author used the camera Canon EOS 1300 D for photographing and Einscan Pro 2X Plus as a 3D scanning device for several years on different kinds of objects made of various materials, including ceramic, stone, glass and metal.

This paper showed that both approaches create 3D models with high resolution in easy and different ways.

Handling objects and preparing them for photographing or scanning has involved a number of caveats and challenges regarding the risk of damage that the author had to bear in mind.

This paper is completely based on the author’s personal experiences of creating 3D image of various objects in the project of Documentation of Objects in the Jordanian Archaeological Museums.

This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them.

The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed.

The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general.

The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation.

The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks.

The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.

In this paper, we review the process of “3D modeling” and “model‐based recognition” along with their potential industrial applications. We put a particular emphasis on the case scenario of robot grasp analysis for which 3D model‐based object recognition seems to be a more palpable choice compared with the conventional tactile sensors solutions. We also put a particular emphasis on the main challenges in the areas of 3D modeling and model‐based recognition and give a brief literature review of the latest research that was carried out to respond to these challenges.

Previous research has shown that “Scan-vs-BIM” object recognition systems, which fuse three dimensional (3D) point clouds from terrestrial laser scanning (TLS) or digital photogrammetry with 4D project building information models (BIM), provide valuable information for tracking construction works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structural works only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking secondary and temporary objects would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. The paper aims to discuss these issues.

Two techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds are implemented and tested using real-life data collected from a reinforced concrete building construction site. Both techniques represent significant innovative extensions of existing “Scan-vs-BIM” object recognition frameworks.

The experimental results show that it is feasible to recognise secondary and temporary objects in TLS point clouds with good accuracy using the two novel techniques; but it is envisaged that superior results could be achieved by using additional cues such as colour and 3D edge information.

This article makes valuable contributions to the problem of detecting and tracking secondary and temporary objects in 3D point clouds. The power of Scan-vs-BIM object recognition approaches to address this problem is demonstrated, but their limitations are also highlighted.

This paper aims to examine the impact of using digitized objects for inquiry with middle-school classrooms. Research analyzed critical thinking processes and student engagement during collaborative investigations with 3D models of authentic objects.

Digitized objects were 3D scans of fossils from the paleontology collection at the Natural History Museum of Utah implemented as 3D prints and 3D virtual models. Verbal protocol analysis examined critical thinking processes during collaborative student learning. Engagement was assessed via student feedback and a classroom observation protocol.

The findings demonstrated that digitized objects facilitated key critical thinking processes, particularly observation, problem finding, elaboration and evaluation. Student feedback was very positive and focused on strong interest in 3D technologies and the ability to engage in authentic exploration. Observations showed very high levels of on-task engagement.

Additional research is necessary to determine if findings generalize across varied learner populations, including broader age ranges and socioeconomic samples, to activities implemented as fully online experiences and to digitized objects from varied domains.

Findings demonstrate digitized objects are effective methods to engage students in critical thinking and to promote engagement with authentic objects during classroom learning. Results demonstrate strong potential of new technologies to leverage the educational impact of digitized objects from local collections, setting the stage for expanded educational outreach by museums and libraries.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

Investigate the use of two imaging‐based methods – coded pattern projection and laser‐based triangulation – to generate 3D models as input to a rapid prototyping pipeline.

Discusses structured lighting technologies as suitable imaging‐based methods. Two approaches, coded‐pattern projection and laser‐based triangulation, are specifically identified and discussed in detail. Two commercial systems are used to generate experimental results. These systems include the Genex Technologies 3D FaceCam and the Integrated Vision Products Ranger System.

Presents 3D reconstructions of objects from each of the commercial systems.

Provides background in imaging‐based methods for 3D data collection and model generation. A practical limitation is that imaging‐based systems do not currently meet accuracy requirements, but continued improvements in imaging systems will minimize this limitation.

Imaging‐based approaches to 3D model generation offer potential to increase scanning time and reduce scanning complexity.

Introduces imaging‐based concepts to the rapid prototyping pipeline.

The purpose of this paper is to present the rationale for democratising the digital reproduction of cultural heritage via “mass photogrammetry”, by providing approaches to digitise objects from cultural heritage collections housed in museums or private spaces using devices and photogrammetry techniques accessible to the public. The paper is intended as a democratised approach rather than as a “scientific approach” for the purpose that mass photogrammetry can be achieved at scale.

The methodology aims to convert the art of photogrammetry into a more mechanical approach by overcoming common difficulties faced within exhibition spaces. This approach is replicable and allows anyone possessing inexpensive equipment with basic knowledge of photogrammetry to achieve acceptable results.

The authors present the experience of acquiring over 300 3D models through photogrammetry from over 25 priority sites and museums in East Asia. The approach covers the entire process from capturing to editing, and importing 3D models into integrated development environments for displays such as interactive 3D, Virtual Reality and Augmented Reality.

The simplistic approach for democratised, mass photogrammetry has implications for stirring public interests in the digital preservation of heritage objects in countries where museums and cultural institutions have little access to digital teams, provided that Intellectual Property issues are cared for. The approach to mass photogrammetry also means that personal cultural heritage objects hidden within the homes of various societies and relics in circulation in the antiques market can be made accessible globally at scale.

This paper focuses on the complete practical nature of photogrammetry conducted within cultural institutions. The authors provide a means for the public to conduct good photogrammetry so that all cultural heritage objects can be digitally recorded and shared globally so as to promote the cross-cultural appreciation of material cultures from the past.