A Mobile Structured Light System for 3D Face Acquisition

Piccirilli, Marco; Doretto, Gianfranco; Ross, Arun; Adjeroh, Donald A.

doi:10.1109/jsen.2015.2511064

Cited by 26 publications

(9 citation statements)

References 5 publications

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“…Although most professional handheld scanners are considered acceptable in terms of their scan image quality, they are expensive and often require considerable training time to learn their complex scanning protocols [ 3 , 24 , 25 ]. Alternatively, 3D sensor cameras based on structured-light technology have been developed for smartphone and tablet devices [ 15 , 26 - 28 ]. An advantage of using mobile devices for face scanning is their user-friendly operation; this reduces the training time for users [ 15 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Mai¹,

Lee²

2020

J Med Internet Res

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Background The accurate assessment and acquisition of facial anatomical information significantly contributes to enhancing the reliability of treatments in dental and medical fields, and has applications in fields such as craniomaxillofacial surgery, orthodontics, prosthodontics, orthopedics, and forensic medicine. Mobile device–compatible 3D facial scanners have been reported to be an effective tool for clinical use, but the accuracy of digital facial impressions obtained with the scanners has not been explored. Objective We aimed to review comparisons of the accuracy of mobile device–compatible face scanners for facial digitization with that of systems for professional 3D facial scanning. Methods Individual search strategies were employed in PubMed (MEDLINE), Scopus, Science Direct, and Cochrane Library databases to search for articles published up to May 27, 2020. Peer-reviewed journal articles evaluating the accuracy of 3D facial models generated by mobile device–compatible face scanners were included. Cohen d effect size estimates and confidence intervals of standardized mean difference (SMD) data sets were used for meta-analysis. Results By automatic database searching, 3942 articles were identified, of which 11 articles were considered eligible for narrative review, with 6 studies included in the meta-analysis. Overall, the accuracy of face models obtained using mobile device–compatible face scanners was significantly lower than that of face models obtained using professional 3D facial scanners (SMD 3.96 mm, 95% CI 2.81-5.10 mm; z=6.78; P<.001). The difference between face scanning when performed on inanimate facial models was significantly higher (SMD 10.53 mm, 95% CI 6.29-14.77 mm) than that when performed on living participants (SMD 2.58 mm, 95% CI 1.70-3.47 mm, P<.001, df=12.94). Conclusions Overall, mobile device–compatible face scanners did not perform as well as professional scanning systems in 3D facial acquisition, but the deviations were within the clinically acceptable range of <1.5 mm. Significant differences between results when 3D facial scans were performed on inanimate facial objects and when performed on the faces of living participants were found; thus, caution should be exercised when interpreting results from studies conducted on inanimate objects.

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Section: Introductionmentioning

confidence: 99%

Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Mai¹,

Lee²

2020

J Med Internet Res

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“…The ability to obtain and reconstruct three‐dimensional (3D) data about objects and scenes is invaluable in numerous applications ranging from quality assessment, reverse engineering, autonomous vehicles, medicine, art and architecture digital archiving as well as remote sensing [1–6]. However, the point cloud or depth map returned from the object or scene scan tends to contain a certain amount of outliers.…”

Section: Introductionmentioning

confidence: 99%

Outlier percentage estimation for shape‐ and parameter‐independent outlier detection

2020

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“…The development of three-dimensional (3D) scanning systems -also known as 3D data acquisition systems -is a longstanding goal in computer vision and an active research topic since 1980's [1], [2] when computers started to become popular. In the last decade we have experienced technology advances and innovations in this field that allowed the emerging of new 3D applications such as robot navigation [3], face recognition and modelling [4], [5], parts inspection (industry) [6], cultural heritage [7], and entertainment (virtual worlds) [8].…”

Section: Introductionmentioning

confidence: 99%

A Simple Geometrical-Based Calibration Technique for 3D Scanners with Rotating Platform

Wispel

Machado

Bayer

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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Active three-dimensional (3D) scanning techniques usually create 3D models of real scenes by using known light patterns reflected by surfaces and captured by cameras. Each scanning technique has its own implementation issues, which can impact on its complexity, processing time and data precision. This paper proposes a simple geometrical-based calibration technique for 3D scanners with rotating platform. As we focus on low-cost and accessibility, the scanning system was implemented using offthe-shelf components. Also its structure was designed to be 3D printed. Two objects are scanned and their point clouds are used to assess the system performance. Results show that the proposed model presents a good trade-off between accuracy and implementation complexity with an average accuracy error equals to 0.196 mm and a mean squared error (MSE) smaller than 4.65%.

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A Mobile Structured Light System for 3D Face Acquisition

Cited by 26 publications

References 5 publications

Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Outlier percentage estimation for shape‐ and parameter‐independent outlier detection

A Simple Geometrical-Based Calibration Technique for 3D Scanners with Rotating Platform

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