Surface remeshing with robust user-guided segmentation

Khan, Dawar; Yan, Dong‐Ming; Ding, Fan; Zhuang, Yixin; Zhang, Xiao-Peng

doi:10.1007/s41095-018-0107-y

Cited by 13 publications

(26 citation statements)

References 34 publications

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“…Studies [30,31,32,33] integrated harmonic field theory with sketchbased segmentation, which possess solid theoretical basis and work well. Khan et al [34] proposed to use a robust interactive segmentation method to improve remeshing quality. In their approach, mesh segmentation is first initialized using an existing interactive method based on the live-wire interaction [35] to well capture sharp features.…”

Section: General Mesh Segmentationmentioning

confidence: 99%

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Zhang

Song

et al. 2020

Graphical Models

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Automatic segmentation of 3D tooth models into individual teeth is an important step in orthodontic CAD systems. 3D tooth segmentation is a mesh instance segmentation task. Complex geometric features on the surface of 3D tooth models often lead to failure of tooth boundary detection, so it is difficult to achieve automatic and accurate segmentation by traditional mesh segmentation methods. We propose a novel solution to address this problem. We map a 3D tooth model isomorphically to a 2D harmonic parameter space and convert it into an image. This allows us to use a CNN to learn a highly robust image segmentation model to achieve automated and accurate segmentation of 3D tooth models. Finally, we map the image segmentation mask back to the 3D tooth model and refine the segmentation result using an improved Fuzzy-Clustering-and-Cuts algorithm. Our method has been incorporated into an orthodontic CAD system, and performs well in practice.

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Section: General Mesh Segmentationmentioning

confidence: 99%

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Zhang

Song

et al. 2020

Graphical Models

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“…3). To solve these issues, we follow a divide-and-conquer approach [42] of segment-based surface remeshing. We divide the mesh into random patches and apply the remeshing operations (RAR method [11]) to each patch independently.…”

Section: Mesh Segmentationmentioning

confidence: 99%

“…A high AR suggests low triangle quality. The smaller value indicates good quality unless AR = 1 [42].…”

Section: Meshing Quality Parametersmentioning

confidence: 99%

Valence optimization and angle improvement for molecular surface remeshing

et al. 2020

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Molecular surface mesh generation plays a vital role in molecular modeling and visualization. However, meshes extracted directly from Protein Data Bank files have several issues such as small and large triangles, redundant elements, selfintersections, and irregular vertices. The state-of-the-art mesh improvement methods often fail to deal with these issues. In this paper, we present a novel method for valence optimization and angle improvement. For valence optimization, we remove the bad valence vertices with its neighbor triangle making regular holes in the mesh. The holes are filled in a careful manner to improve their valences as well as angle quality. We also use a segmentation-based surface remeshing which segments the mesh into random segments and then each segment is independently remeshed. In addition, a point insertion scheme is applied to minimize the ratio of obtuse triangles. Experimental results show that our method not only improves the maximal and minimal angles to an angle bound of [30 • 120 • ] but also improves the vertices' regularity, reduces the ratio of obtuse triangles, preserves the area and volume, and always succeeds with downstream applications.

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“…In regards to mesh optimization, the goal is to reduce the size of the mesh while maintaining the required LOD in each use case. When simplifying and improving the quality of the meshes, the geometry and the topology of the mesh have to be taken into account and many algorithms have been developed for this purpose [23][24][25][26][27][28]. However, it is important to note that the success of the method will depend on the shape of the element one is dealing with.…”

Section: D Data Optimizationmentioning

confidence: 99%

Augmented Reality Markerless Multi-Image Outdoor Tracking System for the Historical Buildings on Parliament Hill

et al. 2019

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Augmented Reality (AR) applications have experienced extraordinary growth recently, evolving into a well-established method for the dissemination and communication of content related to cultural heritage—including education. AR applications have been used in museums and gallery exhibitions and virtual reconstructions of historic interiors. However, the circumstances of an outdoor environment can be problematic. This paper presents a methodology to develop immersive AR applications based on the recognition of outdoor buildings. To demonstrate this methodology, a case study focused on the Parliament Buildings National Historic Site in Ottawa, Canada has been conducted. The site is currently undergoing a multiyear rehabilitation program that will make access to parts of this national monument inaccessible to the public. AR experiences, including simulated photo merging of historic and present content, are proposed as one tool that can enrich the Parliament Hill visit during the rehabilitation. Outdoor AR experiences are limited by factors, such as variable lighting (and shadows) conditions, caused by changes in the environment (objects height and orientation, obstructions, occlusions), the weather, and the time of day. This paper proposes a workflow to solve some of these issues from a multi-image tracking approach.

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Surface remeshing with robust user-guided segmentation

Cited by 13 publications

References 34 publications

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Valence optimization and angle improvement for molecular surface remeshing

Augmented Reality Markerless Multi-Image Outdoor Tracking System for the Historical Buildings on Parliament Hill

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