A planar-reflective symmetry transform for 3D shapes

Podolak, Joshua; Shilane, Philip; Golovinskiy, Aleksey; Rusinkiewicz, Szymon; Funkhouser, Thomas

doi:10.1145/1141911.1141923

Cited by 268 publications

(172 citation statements)

References 38 publications

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“…Region-based methods are mainly conducted using K-means clustering [5], a watershed [6], an active contour [7], and Mask-MCNet [8]. Boundary-based methods include a graph cut [9], random walks [10], and a snake [11]. Among them, the most popular method is to set the initial position of the boundary between teeth and gums using a snake.…”

Section: Related Studiesmentioning

confidence: 99%

Tooth Segmentation of 3D Scan Data Using Generative Adversarial Networks

et al. 2020

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The use of intraoral scanners in the field of dentistry is increasing. In orthodontics, the process of tooth segmentation and rearrangement provides the orthodontist with insights into the possibilities and limitations of treatment. Although, full-arch scan data, acquired using intraoral scanners, have high dimensional accuracy, they have some limitations. Intraoral scanners use a stereo-vision system, which has difficulties scanning narrow interdental spaces. These areas, with a lack of accurate scan data, are called areas of occlusion. Owing to such occlusions, intraoral scanners often fail to acquire data, making the tooth segmentation process challenging. To solve the above problem, this study proposes a method of reconstructing occluded areas using a generative adversarial network (GAN). First, areas of occlusion are eliminated, and the scanned data are sectioned along the horizontal plane. Next, images are trained using the GAN. Finally, the reconstructed two-dimensional (2D) images are stacked to a three-dimensional (3D) image and merged with the data where the occlusion areas have been removed. Using this method, we obtained an average improvement of 0.004 mm in the tooth segmentation, as verified by the experimental results.

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Section: Related Studiesmentioning

confidence: 99%

Tooth Segmentation of 3D Scan Data Using Generative Adversarial Networks

et al. 2020

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“…There exist algorithms which find symmetry in meshes and volume without sampling keypoints. The works described in ( [28], [29], [5], [30], [31], [32], [33], [34], [35]) belong to this category.…”

Section: Related Workmentioning

confidence: 99%

Detecting Approximate Reflection Symmetry in a Point Set Using Optimization on Manifold

Nagar

Raman

2019

IEEE Trans. Signal Process.

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We propose an algorithm to detect approximate reflection symmetry present in a set of volumetrically distributed points belonging to R d containing a distorted reflection symmetry pattern. We pose the problem of detecting approximate reflection symmetry as the problem of establishing correspondences between the points which are reflections of each other and we determine the reflection symmetry transformation. We formulate an optimization framework in which the problem of establishing the correspondences amounts to solving a linear assignment problem and the problem of determining the reflection symmetry transformation amounts to solving an optimization problem on a smooth Riemannian product manifold. The proposed approach estimates the symmetry from the geometry of the points and is descriptor independent. We evaluate the performance of the proposed approach on the standard benchmark dataset and achieve the state-of-the-art performance. We further show the robustness of our approach by varying the amount of distortion in a perfect reflection symmetry pattern where we perturb each point by a different amount of perturbation. We demonstrate the effectiveness of the method by applying it to the problem of 2-D and 3-D reflection symmetry detection along with comparisons.

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“…Other approaches for aligning the models have been proposed. Chaouch and Verroust‐Blondet used a symmetry‐based approach that is based on two types of symmetries of the models: the reflective symmetry and the local translational symmetry along a direction. Zhang and Jia proposed an optimization‐based approach that uses a Graphics Processing Unit (GPU)‐based genetic algorithms exhaustively search for the optimal alignment between two models.…”

Section: Related Workmentioning

confidence: 99%

Fast accessing Web3D contents using lightweight progressive meshes

Wen

Xie

Jia

2015

Computer Animation & Virtual

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Accessing Web3D contents is relatively slow through Internet under limited bandwidth. Preprocessing of 3D models can certainly alleviate the problem, such as 3D compression and progressive meshes (PM). But none of them considers the similarity between components of a 3D model, so that we could take advantage of this to further improve the efficiency. This paper proposes a similarity-aware data reduction method together with PM, called lightweight progressive meshes (LPM). LPM aims to excavate similar components in a 3D model, generates PM representation of each component left after removing redundant components, and organizes all the processed data using a structure called lightweight scene graph. The proposed LPM possesses four significant advantages. First, it can minimize the file size of 3D model dramatically without almost any precision loss. Because of this, minimal data is delivered. Second, PM enables the delivery to be progressive, so called streaming. Third, when rendering at client side, due to lightweight scene graph, decompression is not necessary and instanced rendering is fully exerted. Fourth, it is extremely efficient and effective under very limited bandwidth, especially when delivering large 3D scenes. Performance on real data justifies the effectiveness of our LPM, which improves the state-of-the-art in accessing Web3D contents.

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A planar-reflective symmetry transform for 3D shapes

Cited by 268 publications

References 38 publications

Tooth Segmentation of 3D Scan Data Using Generative Adversarial Networks

Tooth Segmentation of 3D Scan Data Using Generative Adversarial Networks

Detecting Approximate Reflection Symmetry in a Point Set Using Optimization on Manifold

Fast accessing Web3D contents using lightweight progressive meshes

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