# PREMA: Part-based REcurrent Multi-view Aggregation Network for 3D Shape Retrieval

arXiv: https://arxiv.org/abs/2111.04945 · HTML: https://mlsanity.com/paper/2111.04945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [SO-Net: Self-Organizing Network for Point Cloud Analysis](https://mlsanity.com/paper/1803.04249/) (2018)
- [Generative and Discriminative Voxel Modeling with Convolutional Neural Networks](https://mlsanity.com/paper/1608.04236/) (2016)
- [GIFT: A Real-time and Scalable 3D Shape Search Engine](https://mlsanity.com/paper/1604.01879/) (2016)
- [A Deeper Look at 3D Shape Classifiers](https://mlsanity.com/paper/1809.02560/) (2018)
- [View N-gram Network for 3D Object Retrieval](https://mlsanity.com/paper/1908.01958/) (2019)
- [Pairwise Decomposition of Image Sequences for Active Multi-View Recognition](https://mlsanity.com/paper/1605.08359/) (2016)
- [Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition](https://mlsanity.com/paper/1903.08923/) (2019)
- [Learning Discriminative 3D Shape Representations by View Discerning Networks](https://mlsanity.com/paper/1808.03823/) (2018)

## Built on by (most cited first, 1 of 1)

- [Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding](https://mlsanity.com/paper/2312.16477/) (2023)

## Abstract (arXiv)

We propose the Part-based Recurrent Multi-view Aggregation network(PREMA) to eliminate the detrimental effects of the practical view defects, such as insufficient view numbers, occlusions or background clutters, and also enhance the discriminative ability of shape representations. Inspired by the fact that human recognize an object mainly by its discriminant parts, we define the multi-view coherent part(MCP), a discriminant part reoccurring in different views. Our PREMA can reliably locate and effectively utilize MCPs to build robust shape representations. Comprehensively, we design a novel Regional Attention Unit(RAU) in PREMA to compute the confidence map for each view, and extract MCPs by applying those maps to view features. PREMA accentuates MCPs via correlating features of different views, and aggregates the part-aware features for shape representation.