# R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition

arXiv: https://arxiv.org/abs/2211.11085 · HTML: https://mlsanity.com/paper/2211.11085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [ResMLP: Feedforward networks for image classification with data-efficient training](https://mlsanity.com/paper/2105.03404/) (2021)
- [Global Filter Networks for Image Classification](https://mlsanity.com/paper/2107.00645/) (2021)
- [Volumetric and Multi-View CNNs for Object Classification on 3D Data](https://mlsanity.com/paper/1604.03265/) (2016)
- [MVTN: Multi-View Transformation Network for 3D Shape Recognition](https://mlsanity.com/paper/2011.13244/) (2020)
- [AS-MLP: An Axial Shifted MLP Architecture for Vision](https://mlsanity.com/paper/2107.08391/) (2021)
- [S2-MLP: Spatial-Shift MLP Architecture for Vision](https://mlsanity.com/paper/2106.07477/) (2021)
- [PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition](https://mlsanity.com/paper/1808.07659/) (2018)
- [MVT: Multi-view Vision Transformer for 3D Object Recognition](https://mlsanity.com/paper/2110.13083/) (2021)
- [GIFT: A Real-time and Scalable 3D Shape Search Engine](https://mlsanity.com/paper/1604.01879/) (2016)
- [Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition](https://mlsanity.com/paper/1906.01592/) (2019)
- [S2-MLPv2: Improved Spatial-Shift MLP Architecture for Vision](https://mlsanity.com/paper/2108.01072/) (2021)
- [Pairwise Decomposition of Image Sequences for Active Multi-View Recognition](https://mlsanity.com/paper/1605.08359/) (2016)
- [Rethinking Token-Mixing MLP for MLP-based Vision Backbone](https://mlsanity.com/paper/2106.14882/) (2021)

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

- [Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding](https://mlsanity.com/paper/2312.16477/) (2023)
- [3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review](https://mlsanity.com/paper/2306.05978/) (2023)

## Abstract (arXiv)

Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community. MLP-like models achieve competitive performance on a single 2D image classification with less inductive bias without hand-crafted convolution layers. In this work, we explore the effectiveness of MLP-based architecture for the view-based 3D object recognition task. We present an MLP-based architecture termed as Round-Roll MLP (R2-MLP). It extends the spatial-shift MLP backbone by considering the communications between patches from different views. R2-MLP rolls part of the channels along the view dimension and promotes information exchange between neighboring views. We benchmark MLP results on ModelNet10 and ModelNet40 datasets with ablations in various aspects. The experimental results show that, with a conceptually simple structure, our R2-MLP achieves competitive performance compared with existing state-of-the-art methods.