3D Symmetry Detection Using The Extended Gaussian Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Identification of a 3-D Object Represented by Octree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Similarity Search on Voxelized CAD Objects
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Symmetry descriptors and 3D shape matching
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Accurate detection of symmetries in 3D shapes
ACM Transactions on Graphics (TOG)
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
Content-Based 3D Object Retrieval
IEEE Computer Graphics and Applications
Graphical Models
International Journal of Computer Vision
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
Efficient 3-D model search and retrieval using generalized 3-D radon transforms
IEEE Transactions on Multimedia
Combining Topological and Geometrical Features for Global and Partial 3-D Shape Retrieval
IEEE Transactions on Multimedia
SHREC'09 track: generic shape retrieval
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
3D model retrieval using hybrid features and class information
Multimedia Tools and Applications
SymPan: 3D model pose normalization via panoramic views and reflective symmetry
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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In this paper, a novel framework for 3D object retrieval is presented. The paper focuses on the investigation of an accurate 3D model alignment method, which is achieved by combining two intuitive criteria, the plane reflection symmetry and rectilinearity. After proper positioning in a coordinate system, a set of 2D images (multi-views) are automatically generated from the 3D object, by taking views from uniformly distributed viewpoints. For each image, a set of flip-invariant shape descriptors is extracted. Taking advantage of both the pose estimation of the 3D objects and the flip-invariance property of the extracted descriptors, a new matching scheme for fast computation of 3D object dissimilarity is introduced. Experiments conducted in SHREC 2009 benchmark show the superiority of the pose estimation method over similar approaches, as well as the efficiency of the new matching scheme.