PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in
Point Clouds

PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds

24 October 2022

Jonathon Shlens

Mingxing Tan

Papers citing "PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds"

7 / 7 papers shown

Title
Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection Shuai Zeng Wenzhao Zheng Jiwen Lu Haibin Yan 3DPC 23 1 0 27 May 2024
A Survey of Label-Efficient Deep Learning for 3D Point Clouds Aoran Xiao Xiaoqin Zhang Ling Shao Shijian Lu 3DPC 29 18 0 31 May 2023
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations Zhaoqi Leng Guowang Li Chenxi Liu E. D. Cubuk Pei Sun Tong He Drago Anguelov Mingxing Tan 3DPC 33 9 0 24 Oct 2022
Regularization Strategy for Point Cloud via Rigidly Mixed Sample Dogyoon Lee Jaeha Lee Junhyeop Lee Hyeongmin Lee Minhyeok Lee Sungmin Woo Sangyoun Lee 3DPC 148 74 0 03 Feb 2021
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection Shaoshuai Shi Li Jiang Jiajun Deng Zhe Wang Chaoxu Guo Jianping Shi Xiaogang Wang Hongsheng Li 3DPC 137 404 0 31 Jan 2021
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation Golnaz Ghiasi Yin Cui A. Srinivas Rui Qian Tsung-Yi Lin E. D. Cubuk Quoc V. Le Barret Zoph ISeg 226 968 0 13 Dec 2020
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,099 0 02 Dec 2016