Multimedia Tools and Applications

, Volume 78, Issue 12, pp 15951–15995 | Cite as

3D convolutional neural network for object recognition: a review

  • Rahul Dev SinghEmail author
  • Ajay Mittal
  • Rajesh K. Bhatia


Recognition of an object from an image or image sequences is an important task in computer vision. It is an important low-level image processing operation and plays a crucial role in many real-world applications. The challenges involved in object recognition are multi-model, multi-pose, complicated background, and depth variations. Recently developed methods have dealt with these challenges and have reported remarkable results for 3D objects. In this paper, a comprehensive overview of recent advances in 3D object recognition using Convolutional Neural Networks (CNN) has been presented. Along with the latest progress in 3D images, general overview of object recognition of 2D, 2.5D, and 3D images is presented.


Deep learning 3D images Convolutional neural network Object recognition Supervised learning 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Punjab Engineering CollegeChandigarhIndia
  2. 2.UIETPanjab UniversityChandigarhIndia

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