Microdisplay-Based Intraoral 3D Scanner for Dentistry

Riehemann, S.; Palme, Martin; Kuehmstedt, Peter; Großmann, Constanze; Notni, Gunther; Hintersehr, J

doi:10.1109/jdt.2010.2096799

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Three-dimensional models are ubiquitous data in the form of 3D surface meshes, point clouds, volumetric data, etc. in a wide variety of domains such as material and mechanical engineering [1], genetics [2], molecular biology [3], entomology [4], and dentistry [5,6], to name a few. Processing such large datasets (e.g., shape retrieval, matching, or recognition) is computationally expensive and memory intensive.…”

Section: Introductionmentioning

confidence: 99%

An Application of Manifold Learning in Global Shape Descriptors

et al. 2019

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With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific group of techniques in statistics and machine learning, known as manifold learning, to develop a global shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant descriptor. The extensive experiments performed in this study show that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace-Beltrami operator. The proposed method significantly outperforms state-of-the-art algorithms on several non-rigid shape retrieval benchmarks.

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Section: Introductionmentioning

confidence: 99%

An Application of Manifold Learning in Global Shape Descriptors

et al. 2019

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“…3D models have become an ubiquitous data type in various fields such as material and mechanical engineering [OTF*16], genetics [NPK*07], molecular biology [GRP*16], dentistry [RPK*11, WBG*16], etc. Additionally, modern scanning devices such as Microsoft kinect [HSXS13] and laser scanners have made it possible to generate 3D models more efficiently and accurately.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data‐Driven 3D Shape Descriptors

Rostami

Bashiri

Rostami

et al. 2018

Computer Graphics Forum

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Recent advances in scanning device technologies and improvements in techniques that generate and synthesize 3D shapes have made 3D models widespread in various fields including medical research, biology, engineering, etc. 3D shape descriptors play a fundamental role in many 3D shape analysis tasks such as point matching, establishing point‐to‐point correspondence, shape segmentation and labelling, and shape retrieval to name a few. Various methods have been proposed to calculate succinct and informative descriptors for 3D models. Emerging data‐driven techniques use machine learning algorithms to construct accurate and reliable shape descriptors. This survey provides a thorough review of the data‐driven 3D shape descriptors from the machine learning point of view and compares them in different criteria. Also, a comprehensive taxonomy of the existing descriptors is proposed based on the exploited machine learning algorithms. Advantages and disadvantages of each category have been discussed in detail. Besides, two alternative categorizations from the data type and the application perspectives are presented. Finally, some directions for possible future research are also suggested.

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“…The lack of engineering in the delivery of the dental implant has paved the way for the new frontier of dental implants. [1][2][3][4][5] Hence, 3-dimensional scanning technology is not only expected to be used for dentistry but also to be applied for other medical fields. In particular, many researchers reported that various fringe projection methods were applied for getting three-dimensional images.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of the fringe projection method, micro display or illumination parts can be needed to project on the teeth and reflective powder also can be needed depending on the light source. [1][2][3][4][5] The fringe projection method is not appropriate for scanning a small region of the inner mouth because a beam projector is needed to project patterns onto teeth. A beam projector has more complex structure and system performance, and user convenience can be restricted by total weight and resolution of displays.…”

Section: Introductionmentioning

confidence: 99%

Development of Image-space Telecentric Lens for Intra-Oral 3D Scanner

Kim¹,

Shin²,

Chang³

et al. 2015

Journal of the Optical Society of Korea

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An image-space telecentric lens for an intra-oral 3D scanner was designed and fabricated for dental application. Since a telecentric function can provide the same results regardless of image plane position, it helps to realize a more accurate image for an intra-oral scanner. The performance of the designed lens meets the required properties for HD resolution. In particular, lateral color is corrected within 1 pixel. This system achieves depth of focus of more than 3 mm. For user convenience, the developed system consists of a prism part and an imaging part. Both parts are optimized to reduce the front size and weight of the system. In order to make the parallax sights, parallax angle was determined to be 8 degrees between two optical systems.

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Microdisplay-Based Intraoral 3D Scanner for Dentistry

Cited by 11 publications

References 10 publications

An Application of Manifold Learning in Global Shape Descriptors

An Application of Manifold Learning in Global Shape Descriptors

A Survey on Data‐Driven 3D Shape Descriptors

Development of Image-space Telecentric Lens for Intra-Oral 3D Scanner

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