Fast and robust computation of molecular surfaces
Proceedings of the eleventh annual symposium on Computational geometry
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Graphics Gems
Using shape distributions to compare solid models
Proceedings of the seventh ACM symposium on Solid modeling and applications
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Effective Similarity Search on Voxelized CAD Objects
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Techniques for Efficiently Searching in Spatial, Temporal, Spatio-temporal, and Multimedia Databases
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Towards Building a Knowledge Base for Research on Andean Weaving
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
A GPU based high-efficient and accurate optimal pose alignment approach of 3D objects
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
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With fast evolving resources for 3D objects such as the Protein Data Bank (PDB) or the World Wide Web, new techniques, so-called similarity models to efficiently and effectively search for these 3D objects become indispensible. Invariances w.r.t. specific geometric transformations such as scaling, translation, and rotation are important features of similarity models. In this paper, we focus on rotation invariance. We first propose a new method of representing objects more accurately in the context of rotation invariance than the well-known voxelization technique.In addition, we extend existing feature-based similarity models by proposing a new spherical partitioning of the data objects based on proportionality and redundancy, and generalizing an existing method for feature extraction. A broad experimental evaluation compares our method with existing methods in terms of accuracy and efficiency. In particular, we experimentally confirm that our point sampling method is better suited to represent 3D objects in the context of rotation invariance than voxelized representations. In addition, we empirically show that our new similarity model significantly outperfoms competitive rotation invariant models in terms of accuracy as well as efficiency.