[PDF] Features For 3d Object Retrieval eBook

Author : Cristina González Delgado
Publisher :
Page : pages
File Size : 22,55 MB
Release : 2016
Category :
ISBN :

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This project is focused on the object retrieval challenging field, a critical area for robotics and computer vision systems. Specifically, this project is trying to address some issues in the object recognition area, which is a key during the object retrieval process. Recently, low cost RGB-D sensors able to capture not only color information but also depth data, have emerged. This is a great opportunity to increment efficiency and robustness of object retrieval applications by taking advantage of this additional depth information. In this project I implemented an evaluation system of 3D feature descriptors by using the Point Cloud Library (PCL) , and used it to perform an specific assessment for two of the most widely used descriptors for object recognition: SHOTColor and PFHColor. They were tested in two different datasets containing data extracted with the Kinnect RGB-D sensor. The first dataset consist of 3D point clouds of individual objects and synthetically built scenes, the second one has 3D point clouds corresponding to individual model objects and scenes from the real world. The evaluation process was done considering the following three properties: descriptiveness, robustness to Gaussian noise impact and support radius variation, and efficiency. As a final result, the outcome of this evaluation process is the performance assessment on those descriptors depending on the different scenarios. SHOTColor descriptors performed better in terms of descriptiveness, robustness to Gaussian noise impact and support radius variations, and efficiency as well. But, they required higher storage capacity than PFHColor descriptors. SHOTColor descriptors seems to be more sensitive to clutter and occlussion effects, since the performance for the real scenes dataset is worsening faster than the PFHColor one. Given those considerations, SHOTColor descriptors are recommended as the best option for time-crucial applications and also for those ones requiring a strong descriptiveness power. On the other hand, PFHColor descriptors are better for spacial-crucial applications.

Author : Yue Gao
Publisher : Morgan Kaufmann
Page : 0 pages
File Size : 38,18 MB
Release : 2014-12-08
Category : Computers
ISBN : 9780128024195

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Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic. View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications.

Author : Yue Gao
Publisher : Morgan Kaufmann
Page : 154 pages
File Size : 34,97 MB
Release : 2014-12-04
Category : Computers
ISBN : 0128026235

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Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic. View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications. Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions Presents the progression from general image retrieval techniques to view-based 3-D object retrieval Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications

Author : Hedi Tabia
Publisher :
Page : 146 pages
File Size : 42,24 MB
Release : 2011
Category :
ISBN :

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Three dimensional object representations have become an integral part of modern computer graphic applications such as computer-aided design, game development and audio-visual production. At the Meanwhile, the 3D data has also become extremely common in fields such as computer vision, computation geometry, molecular biology and medicine. This is due to the rapid evolution of graphics hardware and software development, particularly the availability of low cost 3D scanners which has greatly facilitated 3D model acquisition, creation and manipulation. Content-based search is a necessary solution for structuring, managing these multimedia data, and browsing within these data collections. In this context, we are looking for a system that can automatically retrieve the 3D-models visually similar to a requested 3D-object. Existing solutions for 3D-shape retrieval and classification suffer from high variability towards shape-preserving transformations like affine or isometric transformations (non-rigid transformations). In this context, the aim of my research is to develop a system that can automatically retrieve quickly and with precision 3D models visually similar to a 3D-object query. The system has to be robust to non-rigid transformation that a shape can undergo.During my PhD thesis:We have developed a novel approach to match 3D objects in the presence of nonrigid transformation and partially similar models. We have proposed to use a new representation of 3D-surfaces using 3D curves extracted around feature points. Tools from shape analysis of curves are applied to analyze and to compare curves of two 3D-surfaces. We have used the belief functions, as fusion technique, to define a global distance between 3D-objects. We have also experimented this technique in the retrieval and classification tasks. We have proposed the use of Bag of Feature techniques in 3D-object retrieval and classification.

Author : Philip Shilane
Publisher :
Page : 176 pages
File Size : 22,76 MB
Release : 2008-10-01
Category : Computers
ISBN : 9783639091434

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In recent years, there has been enormous growth in the number of 3D models and their availability to a wide segment of the population. Examples include the National Design Repository which stores 3D computer-aided design (CAD) models, the Protein Data Bank (PDB) that has atomic positions for protein molecules, and the Princeton Shape Benchmark with thousands of everyday objects represented as polygonal surface models. With the availability of free interactive tools for creating 3D models and graphics cards for home computers, we can expect 3D data to become ever more widely available. The first contribution of this research is an analysis technique to select the most important or distinctive regions of an object. Our approach identifies regions of a surface that have shape consistent with objects of the same type and different from objects of other types. The second contribution is a new methodology to analyze shape retrieval methods with a common data set of classified 3D models and software tools called the Princeton Shape Benchmark. This text should be especially useful to researchers in computer graphics, computer vision, CAD, and information retrieval.