ACM Transactions on Graphics (TOG)
Laplace-spectra as fingerprints for shape matching
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spectral approach to shape-based retrieval of articulated 3D models
Computer-Aided Design
Pose-Oblivious Shape Signature
IEEE Transactions on Visualization and Computer Graphics
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Global intrinsic symmetries of shapes
SGP '08 Proceedings of the Symposium on Geometry Processing
Interior distance using barycentric coordinates
SGP '09 Proceedings of the Symposium on Geometry Processing
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape comparison through mutual distances of real functions
Proceedings of the ACM workshop on 3D object retrieval
Volumetric heat kernel signatures
Proceedings of the ACM workshop on 3D object retrieval
3D shape classification using commute time
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
| Hi-index | 0.00 |
This paper introduces a volume-based shape descriptor that is robust with respect to changes in pose and topology. We use modified shape distributions of [OFCD02] in conjunction with the interior distances and barycentroid potential that are based on barycentric coordinates [RLF09]. In our approach, shape distributions are aggregated throughout the entire volume contained within the shape thus capturing information conveyed by the volumes of shapes. Since interior distances and barycentroid potential are practically insensitive to various poses/deformations and to non-pervasive topological changes (addition of small handles), our shape descriptor inherits such insensitivity as well. In addition, if any other modes of information (e.g. electrostatic potential within the protein volume) are available, they can be easily incorporated into the descriptor as additional dimensions in the histograms. Our descriptor has a connection to an existing surface based shape descriptor, the Global Point Signatures (GPS) [Rus07]. We use this connection to fairly examine the value of volumetric information for shape retrieval.We find that while, theoretically, strict isometry invariance requires concentrating on the intrinsic surface properties alone, yet, practically, pose insensitive shape retrieval still can be achieved/enhanced using volumetric information.