body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices

Trujillo‐Jiménez, Magda Alexandra; Navarro, Pablo; Pazos, Bruno; Morales, Leonardo; Ramallo, Virgínia; Paschetta, Carolina; Azevedo, Soledad de; Ruderman, Anahí; Pérez, Orlando J.; Delrieux, Claudio; González‐José, Rolando

doi:10.3390/jimaging6090094

Cited by 14 publications

(10 citation statements)

References 26 publications

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“…The use of low-cost sensors for photogrammetric purposes is an important research topic since it is used in multiple application fields, such as robotics, mobile mapping, digitization of Cultural Heritage (CH), etc. [1][2][3][4][5]. In recent years, omnidirectional cameras, i.e., cameras with a 360 • field of view (FOV) in the horizontal plane, are becoming more and more widespread.…”

Section: Introductionmentioning

confidence: 99%

Use of Low-Cost Spherical Cameras for the Digitisation of Cultural Heritage Structures into 3D Point Clouds

et al. 2022

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The digitization of Cultural Heritage is an important activity for the protection, management, and conservation of structures of particular historical and architectural interest. In this context, the use of low-cost sensors, especially in the photogrammetric field, represents a major research challenge. In this paper, the use of cameras capable of capturing a 360° scene with a single image was assessed. By using spherical photogrammetry and the algorithm based on the structure from motion and multi-view stereo, it is possible to reconstruct the geometry (point cloud) of an object or structure. In particular, for this experiment, the Ricoh theta SC2 camera was used. The analysis was conducted on two sites: one in the laboratory and another directly in the field for the digitization of a large structure (Colonada in Buziaș, Romania). In the case study of the laboratory, several tests were carried out to identify the best strategy for reconstructing the 3D model of the observed environment. In this environment, the approach that provided the best result in terms of both detail and dimensional accuracy was subsequently applied to the case study of Colonada in Buziaș. In this latter case study, a comparison of the point cloud generated by this low-cost sensor and one performed by a high-performance Terrestrial Laser Scanner (TLS), showed a difference of 15 centimeters for 80% of the points. In addition, the 3D point cloud obtained from 360° images is rather noisy and unable to construct complex geometries with small dimensions. However, the photogrammetric dataset can be used for the reconstruction of a virtual tour for the documentation and dissemination of Cultural Heritage.

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Section: Introductionmentioning

confidence: 99%

Use of Low-Cost Spherical Cameras for the Digitisation of Cultural Heritage Structures into 3D Point Clouds

et al. 2022

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“…Currently, many modern remote sensing technologies, such as laser scanning technologies, generate three-dimensional point clouds [1][2][3]. These point clouds are a speci c kind of digital imagery.…”

Section: State Of the Artmentioning

confidence: 99%

Method for clustering and identification of objects in laser scanning point clouds using dynamic logic

Milanov

Badenko

Yadykin

et al. 2021

Int J Adv Manuf Technol

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Today there is a gap between a presence of various new equipment on the market which provides streams of various digital data about the environment, in particular in the form of laser scanning point clouds, and the lack of adequate e cient methods and software for information extraction from such data. A solution to the problem of bridging this gap is proposed on the basis of neural modeling eld theory and dynamic logic (DL). We present a DL-based method of extracting and analyzing information from hybrid point clouds, which include not only spatial coordinates and intensity, but also the color of each point, and can be from multiple sources including terrestrial, mobile and airborne laser scanning data. The proposed method is signi cant for creating a fundamental theoretical basis for new application algorithms and software for many new applications, including building information modeling, "smart city" environment, etc. The proposed method is fairly new to solving various problems related to extracting semantically rich information from a nontraditional type of digital data, especially hybrid point clouds created from laser scanning. This method will allow to signi cantly expand the existing boundaries of knowledge in the eld of extraction and analysis of information from various digital data, because neural modeling eld theory and DL can improve the performance of relevant calculations and close the existing gap in analysis of digital images.

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“…An example of this is presented by Smith et al [11], which introduce a method to recover 3D body data from 2D silhouettes from a pair of images. Another example is that of Trujillo-Jiménez et al [12], which propose to perform precise anthropometry from handheld devices, using body2vec: a specially trained neural network that performs body segmentation (and background removal) prior to point cloud estimation and reconstruction from video. They match the estimated reconstructed models against two standards: a "silver" one consisting of LIDAR data, and a "gold" standard consisting of expertprovided AMs.…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

et al. 2022

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Artificial intelligence techniques have been increasingly applied in healthcare to help in many areas, from assisting clinical diagnoses to preventing diseases. In this paper, a machine learning approach to predict cholesterol levels using non-invasive and easy-to-collect data is presented. Specifically, it uses clinical and anthropometric data gathered by nutritionists during weight loss intervention (dieting) periods. The prediction power analysis of different patient variables is aimed at improving both non-invasive diagnosis quality and screening of associated diseases. Moreover, a clustering analysis has been carried out to identify different groupings of patients that might share some characteristics that have so far remained inconspicuous but might contain a valuable diagnosis or prognosis information for clinical experts.The experiments show a mean absolute percentage error rate (MAPE) of 4.39% in cholesterol estimation via regression, as well as clustering of patients within four profiles in which variable values share commonalities among cluster members.INDEX TERMS digital health assessment, 3D body reconstruction, clinical data regression, patient data clustering, pattern recognition.

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body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices

Cited by 14 publications

References 26 publications

Use of Low-Cost Spherical Cameras for the Digitisation of Cultural Heritage Structures into 3D Point Clouds

Use of Low-Cost Spherical Cameras for the Digitisation of Cultural Heritage Structures into 3D Point Clouds

Method for clustering and identification of objects in laser scanning point clouds using dynamic logic

A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

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