ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2211.06841
  4. Cited By
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud
  Learning

Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

13 November 2022
Yabin Zhang
Jiehong Lin
Ruihuang Li
K. Jia
Lei Zhang
    3DPC
ArXivPDFHTML

Papers citing "Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning"

13 / 13 papers shown
Title
To Supervise or Not to Supervise: Understanding and Addressing the Key
  Challenges of 3D Transfer Learning
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of 3D Transfer Learning
Souhail Hadgi
Lei Li
M. Ovsjanikov
14
0
0
26 Mar 2024
Self-Supervised Learning for Point Clouds Data: A Survey
Self-Supervised Learning for Point Clouds Data: A Survey
Changyu Zeng
W. Wang
A. Nguyen
Yutao Yue
3DPC
19
0
0
09 May 2023
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Ben Fei
Weidong Yang
Liwen Liu
Tian-jian Luo
Rui Zhang
Yixuan Li
Ying He
3DPC
16
17
0
08 May 2023
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Yaohua Zha
Jinpeng Wang
Tao Dai
Bin Chen
Zhi Wang
Shutao Xia
VLM
38
45
0
14 Apr 2023
Masked Autoencoders in 3D Point Cloud Representation Learning
Masked Autoencoders in 3D Point Cloud Representation Learning
Jincen Jiang
Xuequan Lu
Lizhi Zhao
Richard Dazeley
Meili Wang
3DPC
ViT
49
28
0
04 Jul 2022
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud
  Pre-training
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Renrui Zhang
Ziyu Guo
Rongyao Fang
Bingyan Zhao
Dong Wang
Yu Qiao
Hongsheng Li
Peng Gao
3DPC
169
241
0
28 May 2022
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
Cheng Zhang
Jian Shi
X. Deng
Zizhao Wu
3DPC
20
8
0
21 Mar 2022
Benchmarking Robustness of 3D Point Cloud Recognition Against Common
  Corruptions
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Jiachen Sun
Qingzhao Zhang
B. Kailkhura
Zhiding Yu
Chaowei Xiao
Z. Morley Mao
3DPC
31
83
0
28 Jan 2022
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
258
7,337
0
11 Nov 2021
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
Tiange Xiang
Chaoyi Zhang
Yang Song
Jianhui Yu
Weidong (Tom) Cai
3DPC
138
282
0
04 May 2021
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Zaiwei Zhang
Rohit Girdhar
Armand Joulin
Ishan Misra
3DPC
120
267
0
07 Jan 2021
PointContrast: Unsupervised Pre-training for 3D Point Cloud
  Understanding
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Saining Xie
Jiatao Gu
Demi Guo
C. Qi
Leonidas J. Guibas
Or Litany
3DPC
136
618
0
21 Jul 2020
PointNet: Deep Learning on Point Sets for 3D Classification and
  Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. Qi
Hao Su
Kaichun Mo
Leonidas J. Guibas
3DH
3DPC
3DV
PINN
219
13,886
0
02 Dec 2016
1