SHREC'10 Track: Generic 3D Warehouse

The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a Generic 3D shape benchmark based on the Google 3D Warehouse. Introduction: With the increasing number of 3D models are created everyday and stored in databases. Effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of results of different 3D shape retrieval approaches. Here, we propose a new publicly available 3D shape benchmark based on the Google 3D Warehouse to advance the state of art in 3D shape retrieval Task description: The task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix. Data set: All the 3D models in the generic shape benchmark were acquired by a web crawler from the Google 3D Warehouse. To classify the 3D shape models into a ground truth database, one person based on the Google tags has classified objects into ground truth categories based mainly on visual similarity. In this benchmark, there will be over three thousand 3D models. The file format used to represent the 3D models will be the ASCII Object File Format (*.off). Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2). Please Cite the Paper: SHREC'10 Track: Generic 3D Warehouse., T.P. Vanamali, A. Godil, H. Dutagaci,T. Furuya, Z. Lian, R. Ohbuchi, In: M. Daoudi, T. Schreck, M. Spagnuolo, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2010.

[
  "006:55"
]

[
  "en"
]

[
  "006:045"
]

[
  "Information Technology:Data and informatics",
  "Mathematics and Statistics:Image and signal processing"
]