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A list of 3D computer vision papers with Transformers
| Date | Stars |
|---|---|
| 2026-07-31 | 462 |
| 2026-08-06 | 462 |
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# This repo supplements our [3D Vision with Transformers Survey](https://arxiv.org/abs/2208.04309) Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Ming-Hsuan Yang This repo includes all the 3D computer vision papers with Transformers which are presented in our [paper](https://arxiv.org/abs/2208.04309), and we aim to frequently update the latest relevant papers. <p align="center"> <img src="https://user-images.githubusercontent.com/14073587/183882596-ada49e17-bbd5-4b09-962b-e0ff1d8291c0.png" width="600"> </p> #### Content - [Object Classification](#object-classification)<br> - [3D Object Detection](#3d-object-detection)<br> - [3D Segmentation](#3d-segmentation)<br> - [Complete Scenes Segmentation](#complete-scenes-segmentation)<br> - [Point Cloud Video Segmentation](#point-cloud-video-segmentation)<br> - [Medical Imaging Segmentation](#medical-imaging-segmentation)<br> - [3D Point Cloud Completion](#3d-point-cloud-completion)<br> - [3D Pose Estimation](#3d-pose-estimation)<br> - [Other Tasks](#other-tasks)<br> - [3D Tracking](#3d-tracking)<br> - [3D Motion Prediction](#3d-motion-prediction)<br> - [3D Reconstruction](#3d-reconstruction)<br> - [Point Cloud Registration](#point-cloud-registration)<br> ## Object Classification Group-in-Group Relation-Based Transformer for 3D Point Cloud Learning [**RS 2022**][[PDF](https://www.mdpi.com/2072-4292/14/7/1563/pdf?version=1648109597 )] <br> Masked Autoencoders for Point Cloud Self-supervised Learning [**ECCV 2022**][[PDF](https://arxiv.org/pdf/2203.06604)][[Code](https://github.com/Pang-Yatian/Point-MAE )] <br> 3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification [**T-ITS 2022**][[PDF](https://arxiv.org/pdf/2203.00828 )] <br> LFT-Net: Local Feature Transformer Network for Point Clouds Analysis [**T-ITS 2022**][[PDF](https://ieeexplore.ieee.org/document/9700748/ )] <br> Sewer defect detection from 3D point clouds using a transformer-based deep learning model [**Automation in Construction 2022**][[PDF](https://www.mdpi.com/1424-8220/22/12/4517/pdf?version=1655277701 )] <br> 3d medical point transformer: Introducing convolution to attention networks for medical point cloud analysis [**arXiv 2021**][[PDF](https://arxiv.org/pdf/2112.04863)][[Code](https://github.com/crane-papercode/3dmedpt )] <br> Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [**CVPR 2022**][[PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Point-BERT_Pre-Training_3D_Point_Cloud_Transformers_With_Masked_Point_Modeling_CVPR_2022_paper.pdf)][[Code](https://github.com/lulutang0608/Point-BERT )] <br> CpT: Convolutional Point Transformer for 3D Point Cloud Processing [**ACCVW 2022**][[PDF](https://arxiv.org/pdf/2111.10866 )] <br> PatchFormer: An Efficient Point Transformer With Patch Attention [**CVPR 2022**][[PDF](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_PatchFormer_An_Efficient_Point_Transformer_With_Patch_Attention_CVPR_2022_paper.pdf)] <br> PVT: Point-Voxel Transformer for Point Cloud Learning [**arXiv 2021**][[PDF](https://arxiv.org/pdf/2108.06076.pdf)][[Code](https://github.com/HaochengWan/PVT )] <br> Adaptive Wavelet Transformer Network for 3D Shape Representation Learning [**ICLR 2021**][[PDF](https://openreview.net/pdf?id=5MLb3cLCJY )] <br> Point cloud learning with transformer [**arXiv 2021**][[PDF](https://arxiv.org/pdf/2104.13636 )] <br> 3crossnet: Cross-level cross-scale cross-attention network for point cloud representation [**RA-L 2022**][[PDF](https://arxiv.org/pdf/2104.13053 )] <br> Dual Transformer for Point Cloud Analysis [**IEEE Trans Multimedia**][[PDF](https://arxiv.org/pdf/2104.13044 )] <br> Centroid transformers: Learning to abstract with attention [**arXiv 2021**][[PDF](https://arxiv.org/pdf/2102.08606 )] <br> PCT: Point cloud transformer [**CVPR 2019**][[PDF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Modeling_Point_Clouds_
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