Journal ArticleDOI
Shape retrieval using 3D Zernike descriptors
Marcin Novotni,Reinhard Klein +1 more
TLDR
Practical analysis of 3D Zernike invariants along with algorithms and computational details are provided along with a detailed discussion on influence of the algorithm parameters like the conversion into a volumetric function, number of utilized coefficients, etc.Abstract:
We advocate the usage of 3D Zernike invariants as descriptors for 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under rotation, translation and scaling. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of recently introduced spherical harmonics based descriptors. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance using several quality measures and based on experiments on the Princeton Shape Benchmark.read more
Citations
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Journal ArticleDOI
Feature-based similarity search in 3D object databases
TL;DR: A survey of feature-based methods for 3D retrieval, and a taxonomy for these methods is proposed, which describes the aspects that constitute the similarity among 3D objects and designs algorithms that implement such similarity definitions.
Journal ArticleDOI
Mindboggling morphometry of human brains
Arno Klein,Satrajit S. Ghosh,Satrajit S. Ghosh,Forrest Sheng Bao,Joachim Giard,Yrjö Häme,Eliezer Stavsky,Noah Lee,Brian Rossa,Martin Reuter,Elias Chaibub Neto,Anisha Keshavan +11 more
TL;DR: Mindboggle’s algorithms are evaluated using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist and results are publicly available.
Book ChapterDOI
Deep Learning 3D Shape Surfaces Using Geometry Images
TL;DR: This work qualitatively and quantitatively validate that creating geometry images using authalic parametrization on a spherical domain is suitable for robust learning of 3D shape surfaces, and proposes a way to implicitly learn the topology and structure of3D shapes using geometry images encoded with suitable features.
Journal ArticleDOI
Amp: A modular approach to machine learning in atomistic simulations☆
TL;DR: This paper discusses the modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package ( Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems.
Journal ArticleDOI
A new 3D model retrieval approach based on the elevation descriptor
TL;DR: A novel feature, called elevation descriptor, is proposed for 3D model retrieval, which is invariant to translation and scaling of 3D models and it is robust for rotation.
References
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Michael J. Swain,Dana H. Ballard +1 more
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Proceedings ArticleDOI
Topology matching for fully automatic similarity estimation of 3D shapes
TL;DR: A novel technique is proposed, called Topology Matching, in which similarity between polyhedral models is quickly, accurately, and automatically calculated by comparing Multiresolutional Reeb Graphs (MRGs), which operates well as a search key for 3D shape data sets.