  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.
