A very low-cost 3D scanning system for whole-body imaging

Straub, Jeremy; Kerlin, Scott

doi:10.1117/12.2176561

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Photogrammetry is the underlying principle for 3D body scanners, which can simultaneously capture all sides of the human body. These systems employ multiple cameras arranged in a cylindrical architecture to capture the target anatomy from different viewpoints [ 6 , 7 , 8 ]. Nonetheless, these systems can be cumbersome and expensive, depending on the cameras used.…”

Section: Introductionmentioning

confidence: 99%

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

Di Angelo,

Di Stefano,

Guardiani

et al. 2023

Sensors

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Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm’s skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.

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Section: Introductionmentioning

confidence: 99%

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

Di Angelo,

Di Stefano,

Guardiani

et al. 2023

Sensors

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“…Unlike in the industrial sector, accuracy is not the main parameter to consider when choosing a scanner in the field of human scanning. In most of these applications, accuracy has been identified from tenths of millimeters to below 10 mm [ 10 , 22 ], except in some cases such as movies and animation, when higher accuracy is required [ 23 ]. Indeed, in body scanning, large errors occur due to the non-static nature of the human body and to the occurrence of displacements during the scanning process, such as small movements, breathing, and blinking.…”

Section: Introductionmentioning

confidence: 99%

“…This system is a fast scanner that yields at its best functionality an average error of 0.21 ± 1.27 mm in the case of a static mannequin. Straub and Kerlin [ 22 ] presented a photogrammetric full body scanner using 50 inexpensive cameras, which is able to scan human subjects with an average error of 9.4 mm (0.37 in). The designed scanner in this paper uses the structure-from-motion method to extract a 3D model of the human body.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Low-Cost Human Body 3D Scanner Using 100 Cameras

Zeraatkar

Khalili

2020

J. Imaging

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The human body is one of the most complicated objects to model because of its complex features, non-rigidity, and the time required to take body measurements. Basic technologies available in this field range from small and low-cost scanners that must be moved around the body to large and high-cost scanners that can capture all sides of the body simultaneously. This paper presents an image-based scanning system which employs the structure-from-motion method. The design and development process of the scanner includes its physical structure, electronic components, and the algorithms used for extracting 3D data. In addition to the accuracy, which is one of the main parameters to consider when choosing a 3D scanner, the time and cost of the system are among the most important parameters for evaluating a scanner system in the field of human scanning. Because of the non-static nature of the human body, the scanning time is particularly important. On the other hand, a high-cost system may lead to limited use of such systems. The design developed in this paper, which utilizes 100 cameras, facilitates the acquisition of geometric data in a fraction of a second (0.001 s) and provides the capabilities of large, freestanding scanners at a price akin to that of smaller, mobile ones.shape of the back in scoliosis patients [12,13], evaluation of human skeleton changes in daily life [14], and evaluation of the effect of sports on the human body [11] are examples of 3D scanning in healthcare applications. The extraction of the features of body organs and their reproduction is an example of a 3D scanning application in biomedical engineering [15]. Identifying human facial expressions [16,17] and predicting face attractiveness [18] are other examples of this technology's applications in scanning parts which are smaller than the human body as a whole. The use of 3D scanning in clothing design is another interesting application that is used widely in tailoring [19][20][21].Generally, 3D scanning technologies for body scanning include laser technology, structured light, millimeter waves, infrared waves, and stereophotogrammetry [5]. Each technology has its own costs, advantages, and limitations. Unlike in the industrial sector, accuracy is not the main parameter to consider when choosing a scanner in the field of human scanning. In most of these applications, accuracy has been identified from tenths of millimeters to below 10 mm [10,22], except in some cases such as movies and animation, when higher accuracy is required [23]. Indeed, in body scanning, large errors occur due to the non-static nature of the human body and to the occurrence of displacements during the scanning process, such as small movements, breathing, and blinking. For example, just during quiet breathing in healthy subjects, the breathing amplitude is one-third of a deep breath (approximately 3 cm) based on the average 3D distances of the chest and abdominal wall [24]. These displacements might be different from one person to another, depend on age, posture, and sex [...

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“…The description of different methods of human body 3D scanning are presented in (Remondino, 2003), (Schrotter, 2005), (Straub and Kerlin, 2015), and others. New sensors and techniques are applied for human body 3D data acquisition an analysis such as infrared (Tran et al, 2014) and terahertz (Tian et al, 2017) imaging.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric Techniques for Analysis and Visualization of Changes in 2d and 3d Data: Plastic Surgery Application

Knyaz

Zheltov

Чибуничев

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Accurate measurements of 3D scenes and change detection of spatial distributed data are of great importance in different areas of research and application. For more accurate data analysis it is useful to take into account the all available data from various sensors and sources. The common case is that the different kinds of data available in their own coordinate systems and it is needed to transform all data in common coordinate system. This can be provided by finding correspondence between features in data of different types and different sources. Photogrammetry provides structurally connected 2D and 3D data which gives valuable information about correspondence of 2D and 3D features. The approaches to 2D and 3D data fusion and analysis are proposed which are based on complex processing of 2D and 3D data for changes detection and visualization. The techniques for data fusion are developed. The results of applying the developed techniques are presented for plastic surgery application.

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A very low-cost 3D scanning system for whole-body imaging

Cited by 5 publications

References 29 publications

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

A Fast and Low-Cost Human Body 3D Scanner Using 100 Cameras

Photogrammetric Techniques for Analysis and Visualization of Changes in 2d and 3d Data: Plastic Surgery Application

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