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  Published Paper Details:

  Paper Title

3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING.

  Authors

  Sakthi R V,  Jennisha Christina Martin,  Sivasankari R,  Sangeetha Kalyanaraman

  Keywords

Deep Learning, 3D Images, Depth Fusion, Depth Smoothing,

  Abstract


In the previous couple of years, the provision of 3D content continues to be less than 2D counterpart. Hence many 2D-to-3D image conversion methods are proposed. Methods involving human operators are most successful but also tedious and expensive. Automatic methods that make use of a deterministic 3D scene model, have not yet achieved the identical level of quality for they depend on assumptions that are often violated in practice. Here two types of methods are developed. The primary is predicated on learning some extent mapping from local image/ attributes, like color, spatial position. The second method relies on globally estimating the complete depth map of a question image directly from a repository of 3D images employing a Nearest-neighbour regression type idea. It demonstrates the flexibility and therefore the computational efficiency of the methods on numerous 2D images and discusses their pitfall and benefits.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2104165

  Paper ID - 205584

  Page Number(s) - 1298-1306

  Pubished in - Volume 9 | Issue 4 | April 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Sakthi R V,  Jennisha Christina Martin,  Sivasankari R,  Sangeetha Kalyanaraman,   "3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING.", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 4, pp.1298-1306, April 2021, Available at :http://www.ijcrt.org/papers/IJCRT2104165.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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