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
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