Model-based teeth reconstruction

Wu, Chenglei; Bradley, Derek; Garrido, Pablo; Zollhöfer, Michael; Theobalt, Christian; Groß, Markus; Beeler, Thabo

doi:10.1145/2980179.2980233

Cited by 61 publications

(28 citation statements)

References 48 publications

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“…Other approaches employ either an image‐based mouth synthesis approach [TZS*16a] or only render a coarse geometric textured proxy [GVS*15, TZN*15]. Recently, an approach was proposed that can capture high‐quality teeth geometry from external multiview images, using a statistical prior of teeth structure built from dental scans [WBG*16] (see Fig. ).…”

Section: Beyond Face Reconstructionmentioning

confidence: 99%

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State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

Zollhöfer

Thies

Garrido

et al. 2018

Computer Graphics Forum

305

152

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The computer graphics and vision communities have dedicated long standing efforts in building computerized tools for reconstructing, tracking, and analyzing human faces based on visual input. Over the past years rapid progress has been made, which led to novel and powerful algorithms that obtain impressive results even in the very challenging case of reconstruction from a single RGB or RGB‐D camera. The range of applications is vast and steadily growing as these technologies are further improving in speed, accuracy, and ease of use. Motivated by this rapid progress, this state‐of‐the‐art report summarizes recent trends in monocular facial performance capture and discusses its applications, which range from performance‐based animation to real‐time facial reenactment. We focus our discussion on methods where the central task is to recover and track a three dimensional model of the human face using optimization‐based reconstruction algorithms. We provide an in‐depth overview of the underlying concepts of real‐world image formation, and we discuss common assumptions and simplifications that make these algorithms practical. In addition, we extensively cover the priors that are used to better constrain the under‐constrained monocular reconstruction problem, and discuss the optimization techniques that are employed to recover dense, photo‐geometric 3D face models from monocular 2D data. Finally, we discuss a variety of use cases for the reviewed algorithms in the context of motion capture, facial animation, as well as image and video editing.

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Section: Beyond Face Reconstructionmentioning

confidence: 99%

“…Despite the lack of visual features on the teeth, Wu et al [wbg*16] present a statistical model of teeth shape and a method to fit it to multi‐view imagery. image taken from [WBG*16]. …”

Section: Beyond Face Reconstructionmentioning

confidence: 99%

State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

Zollhöfer

Thies

Garrido

et al. 2018

Computer Graphics Forum

305

152

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“…. , λ d ) 1 Compute edge weights using equations (6) and (7); 2 Construct the sparse, real, and symmetric n × n matrices W , D, and L; 3 Find number of connected components (nConComp) from L; 4 Solve equation (10) for nConComp + d eigenvalues; 5 Sort them in increasing order and leave out nConComp smallest ones; 6 if normalization is required then…”

Section: Algorithm 1: Laplacian Eigenmap-based Scale-invariant Globalmentioning

confidence: 99%

“…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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“…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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Model-based teeth reconstruction

Cited by 61 publications

References 48 publications

State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications

An Application of Manifold Learning in Global Shape Descriptors

A Survey on Data‐Driven 3D Shape Descriptors

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