ProUDA: Progressive unsupervised data augmentation for semi-Supervised 3D object detection on point cloud-Reference-Cited by-同舟云学术

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ProUDA: Progressive unsupervised data augmentation for semi-Supervised 3D object detection on point cloud

Published:2023-06 Issue: Volume:170 Page:64-69
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

An Pei,Liang Junxiong,Ma Tao,Chen Yanfei,Wang Liheng,Ma Jie

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference22 articles.

1. Deep multi-scale and multi-modal fusion for 3D object detection;Guo;Pattern Recognit. Lett.,2021

2. Voxel R-CNN: towards high performance voxel-based 3D object detection;Deng,2021

3. Unsupervised data augmentation for consistency training;Xie,2020

4. SESS: self-ensembling semi-supervised 3D object detection;Zhao,2020

5. PV-RCNN: point-voxel feature set abstraction for 3d object detection;Shi,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Subdivided Mask Dispersion Framework for semi-supervised semantic segmentation;Pattern Recognition Letters;2024-03

2. Sequential visual and semantic consistency for semi-supervised text recognition;Pattern Recognition Letters;2024-02

3. Research on Point Cloud Upsampling Technologies;Journal of Image and Signal Processing;2024

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