Literature Analysis on Three Dimensional Object Recognition using Deep Learning

The subject of 3D object identification (segmentation, detection, and classification) has received much research in the areas of computer vision, graphics, and machine learning. Recently, deep learning algorithms have surpassed traditional methods for 3D segmentation problems because of their success in 2D computer vision. As a result, a number of novel methods have been developed and tested on a range of gold-standard datasets.In order for the pattern recognition system to correctly identify the item, the features must be extracted in a form that is compatible with the chosen identification technique. The location from which the features are collected does not matter. Local feature extraction and global feature extraction are the two halves of the object recognition approach.This paper provides a comprehensive analysis of the most recent advances in deep learning-based 3D object recognition. We review the most popular 3D object recognition models and evaluate their salient features.