Journal ArticleDOI
3DSymm: Robust and Accurate 3D Reflection Symmetry Detection
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TLDR
This work proposes a descriptor-free approach, in which, the problem of reflection symmetry detection as an optimization problem and provide a closed-form solution, and shows that the proposed method achieves state-of-the-art performance on the standard dataset.About:
This article is published in Pattern Recognition.The article was published on 2020-06-01. It has received 17 citations till now. The article focuses on the topics: Reflection symmetry & Optimization problem.read more
Citations
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Journal ArticleDOI
Structural symmetry recognition in planar structures using Convolutional Neural Networks
TL;DR: Wang et al. as discussed by the authors used two convolutional neural networks (CNNs) to identify the symmetry group and symmetry order of planar engineering structures, and two different datasets with labels for symmetric structures are created.
Journal ArticleDOI
A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.
TL;DR: In this paper, a registration framework is proposed for multi-modal medical images, which is based on a combination of deep learning and conventional machine learning methods and automatically classifies the image modality so that the registration can be fully automated.
Journal ArticleDOI
Virtual models in 3D digital reconstruction: detection and analysis of symmetry
TL;DR: This work presents the methodology of detecting symmetry in 3D objects based on the three techniques: Eigenvalues and Eigenvectors, local surface discontinuity, and pixel orientation, and these methods have been modified suitably for the detection of symmetry in CH artifacts.
Journal ArticleDOI
Robust, fast and flexible symmetry plane detection based on differentiable symmetry measure
TL;DR: A novel differentiable symmetry measure, which allows using gradient-based optimization to find symmetry in geometric objects and performs well on perfectly as well as approximately symmetrical objects, and is robust to noise and to missing parts.
Journal ArticleDOI
Combining Appearance and Gradient Information for Image Symmetry Detection
TL;DR: In this article, a stable metric is proposed to extract subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences, and the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries.
References
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Journal ArticleDOI
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Proceedings ArticleDOI
Efficient variants of the ICP algorithm
Szymon Rusinkiewicz,Marc Levoy +1 more
TL;DR: An implementation is demonstrated that is able to align two range images in a few tens of milliseconds, assuming a good initial guess, and has potential application to real-time 3D model acquisition and model-based tracking.
Journal ArticleDOI
Least-Squares Fitting of Two 3-D Point Sets
TL;DR: An algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix, is presented.
Proceedings ArticleDOI
Method for registration of 3-D shapes
Paul J. Besl,Neil David Mckay +1 more
TL;DR: In this paper, the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Proceedings ArticleDOI
The Princeton Shape Benchmark
TL;DR: It is concluded that no single descriptor is best for all classifications, and thus the main contribution of this paper is to provide a framework to determine the conditions under which each descriptor performs best.