Human decision-making often relies on visual information from multiple
perspectives or views. In contrast, machine learning-based object recognition
utilizes information from a single image of the object. However, the
information conveyed by a single image may not be sufficient for accurate
decision-making, particularly in complex recognition problems. The utilization
of multi-view 3D representations for object recognition has thus far
demonstrated the most promising results for achieving state-of-the-art
performance. This review paper comprehensively covers recent progress in
multi-view 3D object recognition methods for 3D classification and retrieval
tasks. Specifically, we focus on deep learning-based and transformer-based
techniques, as they are widely utilized and have achieved state-of-the-art
performance. We provide detailed information about existing deep learning-based
and transformer-based multi-view 3D object recognition models, including the
most commonly used 3D datasets, camera configurations and number of views, view
selection strategies, pre-trained CNN architectures, fusion strategies, and
recognition performance on 3D classification and 3D retrieval tasks.
Additionally, we examine various computer vision applications that use
multi-view classification. Finally, we highlight key findings and future
directions for developing multi-view 3D object recognition methods to provide
readers with a comprehensive understanding of the field.

综述了基于多视图三维物体识别方法在三维分类和检索任务方面的最新进展，聚焦于基于深度学习和基于 Transformer 技术的方法，包括常用的三维数据集、相机配置和视角数量、视角选择策略、预训练卷积神经网络架构、融合策略以及在三维分类和检索任务上的识别性能，同时考察了多视图分类在计算机视觉应用中的各种情况，并凸显了关键发现和未来发展方向。