# Deep Models for Multi-View 3D Object Recognition: A Review

arXiv: https://arxiv.org/abs/2404.15224 · HTML: https://mlsanity.com/paper/2404.15224/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Fruit recognition from images using deep learning](https://mlsanity.com/paper/1712.00580/) (2017)
- [Wide-Area Crowd Counting: Multi-View Fusion Networks for Counting in Large Scenes](https://mlsanity.com/paper/2012.00946/) (2020)
- [MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation](https://mlsanity.com/paper/2103.09863/) (2021)

## Built on by (most cited first, 2 of 2)

- [At FullTilt: Real-Time Open-Set 3D Macromolecule Detection Directly from Tilted 2D Projections](https://mlsanity.com/paper/2604.10766/) (2026)
- [Tiny Collaborative Inference for Occlusion-Robust Object Detection](https://mlsanity.com/paper/2606.02894/) (2026)

## Abstract (arXiv)

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.