Deep Learning Advances in Computer Vision with 3D Data: A Survey

被引:206
|
作者
Ioannidou, Anastasia [1 ]
Chatzilari, Elisavet [1 ]
Nikolopoulos, Spiros [1 ]
Kompatsiaris, Ioannis [1 ]
机构
[1] Ctr Res & Technol Hellas CERTH, Inst Informat Technol, Multimedia Knowledge & Social Media Analyt Lab, 6th Km Charilaou,Thermis Rd,POB 60361, Thessaloniki 57001, Greece
基金
欧盟地平线“2020”;
关键词
3D data; 3D object recognition; 3D object retrieval; 3D segmentation; convolutional neural networks; deep learning; OBJECT RECOGNITION; HYPERSPECTRAL DATA; SURFACE-FEATURE; CLASSIFICATION; RETRIEVAL; REPRESENTATIONS; BENCHMARK; FRAMEWORK; NETWORKS; MODEL;
D O I
10.1145/3042064
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Deep learning has recently gained popularity achieving state-of-the-art performance in tasks involving text, sound, or image processing. Due to its outstanding performance, there have been efforts to apply it in more challenging scenarios, for example, 3D data processing. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required.
引用
收藏
页数:38
相关论文
共 50 条
  • [1] Deep learning based 3D segmentation in computer vision: A survey
    He, Yong
    Yu, Hongshan
    Liu, Xiaoyan
    Yang, Zhengeng
    Sun, Wei
    Anwar, Saeed
    Mian, Ajmal
    [J]. Information Fusion, 2025, 115
  • [2] Deep learning for 3D vision
    Guo, Yulan
    Wang, Hanyun
    Clark, Ronald
    Berretti, Stefano
    Bennamoun, Mohammed
    [J]. IET COMPUTER VISION, 2022, 16 (07) : 567 - 569
  • [3] A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
    Khaled Bayoudh
    Raja Knani
    Fayçal Hamdaoui
    Abdellatif Mtibaa
    [J]. The Visual Computer, 2022, 38 : 2939 - 2970
  • [4] A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
    Bayoudh, Khaled
    Knani, Raja
    Hamdaoui, Faycal
    Mtibaa, Abdellatif
    [J]. VISUAL COMPUTER, 2022, 38 (08): : 2939 - 2970
  • [5] A Survey of 3D Data Analysis and Understanding Based on Deep Learning
    Li H.-S.
    Wu Y.-J.
    Zheng Y.-P.
    Wu X.-Q.
    Cai Q.
    Du J.-P.
    [J]. Jisuanji Xuebao/Chinese Journal of Computers, 2020, 43 (01): : 41 - 63
  • [6] 3D computer vision based on machine learning with deep neural networks: A review
    Vodrahalli, Kailas
    Bhowmik, Achintya K.
    [J]. JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY, 2017, 25 (11) : 676 - 694
  • [7] A Survey of the Application of Deep Learning in Computer Vision
    Liu Yuexia
    Cheng Yunfei
    Wang Wu
    [J]. GLOBAL INTELLIGENCE INDUSTRY CONFERENCE (GIIC 2018), 2018, 10835
  • [8] Hyperbolic Deep Learning in Computer Vision: A Survey
    Mettes, Pascal
    Atigh, Mina Ghadimi
    Keller-Ressel, Martin
    Gu, Jeffrey
    Yeung, Serena
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2024, 132 (09) : 3484 - 3508
  • [9] Advances in solar forecasting: Computer vision with deep learning
    Paletta, Quentin
    Terren-Serrano, Guillermo
    Nie, Yuhao
    Li, Binghui
    Bieker, Jacob
    Zhang, Wenqi
    Dubus, Laurent
    Dev, Soumyabrata
    Feng, Cong
    [J]. ADVANCES IN APPLIED ENERGY, 2023, 11
  • [10] Deep reinforcement learning in computer vision: a comprehensive survey
    Le, Ngan
    Rathour, Vidhiwar Singh
    Yamazaki, Kashu
    Luu, Khoa
    Savvides, Marios
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (04) : 2733 - 2819