Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A Simple Algorithm for Nearest Neighbor Search in High Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Transactions on Graphics (TOG)
3D zernike descriptors for content based shape retrieval
SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Shape-Based Autotagging of 3D Models for Retrieval
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Context-based search for 3D models
ACM SIGGRAPH Asia 2010 papers
Characterizing structural relationships in scenes using graph kernels
ACM SIGGRAPH 2011 papers
Recommendation-based editor for business process modeling
Data & Knowledge Engineering
Autonomous Robots
Proceedings of the 16th International Conference on 3D Web Technology
Journal on Computing and Cultural Heritage (JOCCH)
Reshuffle-based interior scene synthesis
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
| Hi-index | 0.00 |
Text search on libraries of 3D models has traditionally worked poorly, as text annotations on 3D models are often unreliable or incomplete. We attempt to improve the recall of text search by automatically assigning appropriate tags to models. Our algorithm finds relevant tags by appealing to a large corpus of partially labeled example models, which does not have to be preclassified or otherwise prepared. For this purpose we use a copy of Google 3DWarehouse, a library of user contributed models which is publicly available on the Internet. Given a model to tag, we find geometrically similar models in the corpus, based on distances in a reduced dimensional space derived from Zernike descriptors. The labels of these neighbors are used as tag candidates for the model with probabilities proportional to the degree of geometric similarity. We show experimentally that text based search for 3D models using our computed tags can approach the quality of geometry based search. Finally, we describe our 3D model search engine that uses this algorithm.