https://github.com/cuge1995/cvpr-2021-point-cloud-analysis

CVPR 2021 papers focusing on point cloud analysis
https://github.com/cuge1995/cvpr-2021-point-cloud-analysis

3d-point-clouds classification computer-vision cvpr2021 deep-learning detection few-shot paper point-cloud point-cloud-analysis segmentation

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CVPR 2021 papers focusing on point cloud analysis

• Host: GitHub
• URL: https://github.com/cuge1995/cvpr-2021-point-cloud-analysis
• Owner: cuge1995
• License: mit
• Created: 2021-03-02T01:56:30.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-08-01T00:11:15.000Z (over 3 years ago)
• Last Synced: 2025-01-12T22:33:01.526Z (3 months ago)
• Topics: 3d-point-clouds, classification, computer-vision, cvpr2021, deep-learning, detection, few-shot, paper, point-cloud, point-cloud-analysis, segmentation
• Homepage:
• Size: 33.2 KB
• Stars: 9
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# CVPR-2021-point-cloud-analysis

CVPR 2021 papers focusing on point cloud analysis

- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://arxiv.org/pdf/2011.13005.pdf) `registration` `oral`

  - [[Code](https://github.com/ShengyuH/OverlapPredator)]

- [Exploring Data Efficient 3D Scene Understanding with Contrastive Scene Contexts](https://arxiv.org/pdf/2012.09165.pdf) `segmentation` `oral`

  - [[Code](https://sekunde.github.io/project_efficient/)]

- [MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization](https://arxiv.org/pdf/2101.06605.pdf) `Synchronization` `oral`

  - [[Code](https://github.com/huangjh-pub/multibody-sync)]

- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://arxiv.org/pdf/2103.01458.pdf)  `generation`

  - [[Code](https://github.com/luost26/diffusion-point-cloud)]

- [Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion, paper not yet]()

  - [[Code](https://github.com/ShiQiu0419/BAAF-Net)]

- [Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges](https://arxiv.org/pdf/2009.03137.pdf) `dataset`

  - [[Code](https://github.com/QingyongHu/SensatUrban)]

- [Few-shot 3D Point Cloud Semantic Segmentation.]() 

  - [[Code](https://github.com/Na-Z/attMPTI)]

- [Bidirectional Projection Network for Cross Dimension Scene Understanding.]() `oral` `2D/3D`

  - [[Code](https://github.com/wbhu/BPNet)]

- [Self-supervised Geometric Perception.](https://arxiv.org/pdf/2103.03114.pdf) `oral` `registration`

  - [[Code](https://github.com/theNded/SGP)]

- [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration.](https://arxiv.org/pdf/2011.12149.pdf) `registration`

  - [[Code](https://github.com/QingyongHu/SpinNet)]

- [Style-based Point Generator with Adversarial Rendering for Point Cloud Completion.](https://arxiv.org/pdf/2103.02535.pdf)  `completion`

  - [[Code](https://github.com/microsoft/SpareNet)]

- [VRCNet: Variational Relational Point Completion Network.](https://arxiv.org/pdf/2104.10154.pdf)  `completion` `oral` `dataset`

  - [[Code](https://github.com/paul007pl/VRCNet)]

- [Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding.](https://arxiv.org/pdf/2103.07838.pdf) `completion`

- [View-Guided Point Cloud Completion.](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.pdf) `completion`

- [PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths.](https://arxiv.org/pdf/2012.03408.pdf) `completion`

- [Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos.](https://hehefan.github.io/pdfs/p4transformer.pdf)  `point cloud video` `oral`

- [3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding.](https://arxiv.org/pdf/2103.16397.pdf)  `Affordance estimation` `new task` `dataset`

  - [[Code](https://github.com/Gorilla-Lab-SCUT/AffordanceNet)]

- [3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection.](https://arxiv.org/pdf/2012.04355v2.pdf)  `detection` 

  - [[Code](https://github.com/thu17cyz/3DIoUMatch)]

- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception.](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper.pdf)  `oral` `segmentation`

  - [[Code](https://github.com/xinge008/Cylinder3D)]