Review of multi-view 3D object recognition methods based on deep learning

Three-dimensional (3D) object recognition is widely used in automated driving, medical image analysis, virtual/ augmented reality, artificial intelligence robots, and other areas. Deep learning is increasingly being used to solve 3D vision problems. Multi-view 3D object recognition based on the deep learning technique has become one of the rigorously researched topics because it can directly use the pretrained and successful advanced classification network as the backbone network, and views from multiple viewpoints can complement each other's detailed features of the object. However, some challenges still exist in this area. Recently, many methods have been proposed to solve the problems pertaining to this research topic. This paper presents a comprehensive review and classification of the latest developments in the deep learning methods for multi-view 3D object recognition. It also summarizes the results of these methods on a few mainstream datasets, provides an insightful summary, and puts forward enlightening future research directions.

Automated summary

This paper comprehensively reviews and classifies the latest developments in deep learning methods for multi-view 3D object recognition, summarizes results on mainstream datasets, provides insightful conclusions, and proposes enlightening future research directions.