NDLI: Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces

Please wait, while we are loading the content...

Stable topological signatures for points on 3D shapes

Reforming shapes for material-aware fabrication

Hierarchical multiview rigid registration

Tight relaxation of quadratic matching

Fast and exact (Poisson) solvers on symmetric geometries

Dynamic SfM: detecting scene changes from image pairs

Perfect Laplacians for polygon meshes

Homotopic morphing of planar curves

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Texture mapping real-world objects with hydrographics

Sparse non-rigid registration of 3D shapes

Continuous matching via vector field flow

Quaternion Julia set shape optimization

An image degradation model for depth-augmented image editing

Perfect matching quad layouts for manifold meshes

A one-dimensional homologically persistent skeleton of an unstructured point cloud in any metric space

Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces

Robust articulated-ICP for real-time hand tracking

LeSSS: learned shared semantic spaces for relating multi-modal representations of 3D shapes

Unconditionally stable shock filters for image and geometry processing

Can bi-cubic surfaces be class A?

Example based repetitive structure synthesis

Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces

Content Provider	ACM Digital Library
Author	Kalogerakis, Evangelos Huang, Haibin Marlin, Benjamin
Abstract	We present a method for joint analysis and synthesis of geometrically diverse 3D shape families. Our method first learns part-based templates such that an optimal set of fuzzy point and part correspondences is computed between the shapes of an input collection based on a probabilistic deformation model. In contrast to previous template-based approaches, the geometry and deformation parameters of our part-based templates are learned from scratch. Based on the estimated shape correspondence, our method also learns a probabilistic generative model that hierarchically captures statistical relationships of corresponding surface point positions and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical relationships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection. The generative model also yields compact shape descriptors that are used to perform fine-grained classification. Finally, it can be also coupled with the probabilistic deformation model to further improve shape correspondence. We provide qualitative and quantitative evaluations of our method for shape correspondence, segmentation, fine-grained classification and synthesis. Our experiments demonstrate superior correspondence and segmentation results than previous state-of-the-art approaches.
Starting Page	25
Ending Page	38
Page Count	14
ISSN	01677055 14678659
DOI	10.1111/cgf.12694
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-07-06
Publisher Place	Goslar
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Networks and Communications

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in