# View N-gram Network for 3D Object Retrieval

arXiv: https://arxiv.org/abs/1908.01958 · HTML: https://mlsanity.com/paper/1908.01958/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis](https://mlsanity.com/paper/2112.04903/) (2021)
- [N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution](https://mlsanity.com/paper/2211.11436/) (2022)
- [3D VR Sketch Guided 3D Shape Prototyping and Exploration](https://mlsanity.com/paper/2306.10830/) (2023)
- [Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views](https://mlsanity.com/paper/2108.07084/) (2021)
- [AttWalk: Attentive Cross-Walks for Deep Mesh Analysis](https://mlsanity.com/paper/2104.11571/) (2021)
- [Fine-Grained VR Sketching: Dataset and Insights](https://mlsanity.com/paper/2209.10008/) (2022)
- [Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrieval](https://mlsanity.com/paper/2507.21489/) (2025)
- [LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition](https://mlsanity.com/paper/2109.01291/) (2021)
- [Towards 3D VR-Sketch to 3D Shape Retrieval](https://mlsanity.com/paper/2209.10020/) (2022)
- [3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval](https://mlsanity.com/paper/2210.15136/) (2022)
- [SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval](https://mlsanity.com/paper/2307.10601/) (2023)
- [TeDA: Boosting Vision-Lanuage Models for Zero-Shot 3D Object Retrieval via Testing-time Distribution Alignment](https://mlsanity.com/paper/2505.02325/) (2025)
- [A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval](https://mlsanity.com/paper/2109.12299/) (2021)
- [PREMA: Part-based REcurrent Multi-view Aggregation Network for 3D Shape Retrieval](https://mlsanity.com/paper/2111.04945/) (2021)
- [Learning task-specific features for 3D pointcloud graph creation](https://mlsanity.com/paper/2209.00949/) (2022)
- [PReP: Efficient context-based shape retrieval for missing parts](https://mlsanity.com/paper/2410.14245/) (2024)
- [Optimizing Multi-Modal Models for Image-Based Shape Retrieval: The Role of Pre-Alignment and Hard Contrastive Learning](https://mlsanity.com/paper/2603.06982/) (2026)
- [DINO Eats CLIP: Adapting Beyond Knowns for Open-set 3D Object Retrieval](https://mlsanity.com/paper/2604.19432/) (2026)

## Abstract (arXiv)

How to aggregate multi-view representations of a 3D object into an informative and discriminative one remains a key challenge for multi-view 3D object retrieval. Existing methods either use view-wise pooling strategies which neglect the spatial information across different views or employ recurrent neural networks which may face the efficiency problem. To address these issues, we propose an effective and efficient framework called View N-gram Network (VNN). Inspired by n-gram models in natural language processing, VNN divides the view sequence into a set of visual n-grams, which involve overlapping consecutive view sub-sequences. By doing so, spatial information across multiple views is captured, which helps to learn a discriminative global embedding for each 3D object. Experiments on 3D shape retrieval benchmarks, including ModelNet10, ModelNet40 and ShapeNetCore55 datasets, demonstrate the superiority of our proposed method.