Occlusion Problem in 3D Object Detection: A Review

被引：2

作者：

Kandelkar, Apurva ^{[1
]}

Batra, Isha ^{[2
]}

Sharma, Shabnam ^{[1
]}

Malik, Arun ^{[1
]}

机构：

[1] Lovely Profess Univ, Phagwara 144001, Punjab, India

[2] CMR Univ, Bengaluru, Karnataka, India

来源：

INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS, ICICC 2022, VOL 1 | 2023年 / 473卷

关键词：

Robotics; Augmented reality (AR); 3D object; Occlusion problem; TRACKING; RECOGNITION;

D O I：

10.1007/978-981-19-2821-5_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In computer vision, 3D object detection has numerous applications such as robotics, augmented reality (AR), medical field, manufacturing industries, and safe autonomous driving. But the real-object detection may involve various problems such as noise, missing data, and occlusion problem. From past few years, the great progress in 3D object detection has been made. Object recognition and identification in occlusion remain a difficult challenge, despite recent breakthroughs in 3D object detection. The occlusion problem is one of the difficulties in object tracking. The paper highlights a number of research hurdles and open concerns that researchers must address.

引用

页码：299 / 312

页数：14

共 29 条

[1] Anuj L, 2017, 2017 INTERNATIONAL CONFERENCE ON INNOVATIVE MECHANISMS FOR INDUSTRY APPLICATIONS (ICIMIA), P432, DOI 10.1109/ICIMIA.2017.7975652
[2] CNN and HOG based comparison study for complete occlusion handling in human tracking
Aslan, Muhammet Fatih
Durdu, Akif
Sabanci, Kadir
Mutluer, Meryem Afife
[J]. MEASUREMENT, 2020, 158 (158)
[3] 3D object tracking via image sets and depth-based occlusion detection
Chen, Yan
Shen, Yingju
Liu, Xin
Zhong, Bineng
[J]. SIGNAL PROCESSING, 2015, 112 : 146 - 153
[4] Ellipse R-CNN: Learning to Infer Elliptical Object From Clustering and Occlusion
Dong, Wenbo
Roy, Pravakar
Peng, Cheng
Isler, Volkan
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 2193 - 2206
[5] A study of the effect of noise and occlusion on the accuracy of convolutional neural networks applied to 3D object recognition
Garcia-Garcia, Alberto
Garcia-Rodriguez, Jose
Orts-Escolano, Sergio
Oprea, Sergiu
Gomez-Donoso, Francisco
Cazorla, Miguel
[J]. COMPUTER VISION AND IMAGE UNDERSTANDING, 2017, 164 : 124 - 134
[6] 3D object recognition from cluttered and occluded scenes with a compact local feature
Guo, Wulong
Hu, Weiduo
Liu, Chang
Lu, Tingting
[J]. MACHINE VISION AND APPLICATIONS, 2019, 30 (04) : 763 - 783
[7] Guo Y, 2019, INT GEOSCI REMOTE SE, P1252, DOI [10.1109/IGARSS.2019.8898701, 10.1109/igarss.2019.8898701]
[8] He QD, 2021, Arxiv, DOI arXiv:2006.04043
[9] RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
Kanezaki, Asako
Matsushita, Yasuyuki
Nishida, Yoshifumi
[J]. 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 5010 - 5019
[10] KasaeiSH, 2020, IEEEASME T MECHATR

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