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.

三维（3D）物体识别广泛应用于自动驾驶、医学图像分析、虚拟/增强现实、人工智能机器人等领域。深度学习越来越多地用于解决 3D 视觉问题。基于深度学习技术的多视图 3D 对象识别已成为严密研究的课题之一，因为它可以直接使用预训练成功的高级分类网络作为骨干网络，并且来自多个视点的视图可以相互补充物体的细节特征。目的。但是，该领域仍然存在一些挑战。最近，已经提出了许多方法来解决与该研究主题有关的问题。本文对用于多视图 3D 对象识别的深度学习方法的最新发展进行了全面回顾和分类。它还总结了这些方法在一些主流数据集上的结果，提供了有见地的总结，并提出了具有启发性的未来研究方向。