@InProceedings{SchmittSotTelSilCom:2015:3DShDe,
               author = "Schmitt, Wagner and Sotomayor, Jose L. and Telea, Alexandru and 
                         Silva, Cl{\'a}udio T. and Comba, Jo{\~a}o L. D.",
          affiliation = "UFRGS and UFRGS and {University of Groningen} and {New York 
                         University} and UFRGS",
                title = "A 3D Shape Descriptor based on Depth Complexity and Thickness 
                         Histograms",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Shape matching, Shape analysis, Depth complexity, Thickness, 
                         Histograms, Content-based retrieval.",
             abstract = "Geometric models play a vital role in several fields, from the 
                         entertainment industry to scientific applications. To reduce the 
                         high cost of model creation, reusing existing models is the 
                         solution of choice. Model reuse is supported by content- based 
                         shape retrieval (CBR) techniques that help finding the desired 
                         models in massive repositories, many publicly available on the 
                         Internet. Key to efficient and effective CBR techniques are shape 
                         descriptors that accurately capture the characteristics of a shape 
                         and can discriminate between different shapes. We present a 
                         descriptor based on the distribution of two global features 
                         measured in a 3D shape, depth complexity and thickness, which 
                         respectively capture aspects of the geometry and topology of 3D 
                         shapes. The final descriptor, called DCTH (depth complexity and 
                         thickness histogram), is a 2D histogram that is invariant to the 
                         translation, rotation and scale of geometric shapes. We 
                         efficiently implement the DCTH on the GPU, allowing its use in 
                         real-time queries of large model databases. We validate the DCTH 
                         with the Princeton and Toyohashi Shape Benchmarks, containing 1815 
                         and 10000 models respectively. Results show that DCTH can 
                         discriminate meaningful classes of these benchmarks and is fast to 
                         compute and robust against shape transformations and different 
                         levels of subdivision and smoothness.",
  conference-location = "Salvador, BA, Brazil",
      conference-year = "26-29 Aug. 2015",
                  doi = "10.1109/SIBGRAPI.2015.51",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.51",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3JNJL6P",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JNJL6P",
           targetfile = "PID3771963.pdf",
        urlaccessdate = "2024, May 01"
}