Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2546-2555.DOI: 10.11772/j.issn.1001-9081.2022071022
Special Issue: 多媒体计算与计算机仿真; 综述
• Multimedia computing and computer simulation • Previous Articles Next Articles
Yi WANG1,2, Jie XIE1(), Jia CHENG1, Liwei DOU2,3
Received:
2022-07-13
Revised:
2022-11-04
Accepted:
2022-11-07
Online:
2023-01-15
Published:
2023-08-10
Contact:
Jie XIE
About author:
WANG Yi, born in 1981, Ph. D., associate professor. His research interests include machine vision perception, image processing, precision measurement.Supported by:
通讯作者:
谢杰
作者简介:
王一(1981—),男,河北唐山人,副教授,博士,主要研究方向:机器视觉感知、图像处理、精密测量基金资助:
CLC Number:
Yi WANG, Jie XIE, Jia CHENG, Liwei DOU. Review of object pose estimation in RGB images based on deep learning[J]. Journal of Computer Applications, 2023, 43(8): 2546-2555.
王一, 谢杰, 程佳, 豆立伟. 基于深度学习的RGB图像目标位姿估计综述[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2546-2555.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022071022
数据集 | 年份 | 类数 | 训练集图像 | 测试集 | 适用场景 | |
---|---|---|---|---|---|---|
真实 | 合成 | 真实 | ||||
LM | 2012 | 15 | — | 50 000 | 18 273 | 杂乱 |
LM-O | 2014 | 8 | — | 50 000 | 1 214 | 杂乱遮挡 |
T-LESS | 2017 | 30 | 37 584 | 50 000 | 10 080 | 无纹理对称 |
YCB-V | 2018 | 21 | 113 198 | 50 000 | 20 738 | 对称 |
TUD-L | 2018 | 3 | 38 288 | 50 000 | 23 914 | 光照变化 |
Tab. 1 Five pose estimation datasets
数据集 | 年份 | 类数 | 训练集图像 | 测试集 | 适用场景 | |
---|---|---|---|---|---|---|
真实 | 合成 | 真实 | ||||
LM | 2012 | 15 | — | 50 000 | 18 273 | 杂乱 |
LM-O | 2014 | 8 | — | 50 000 | 1 214 | 杂乱遮挡 |
T-LESS | 2017 | 30 | 37 584 | 50 000 | 10 080 | 无纹理对称 |
YCB-V | 2018 | 21 | 113 198 | 50 000 | 20 738 | 对称 |
TUD-L | 2018 | 3 | 38 288 | 50 000 | 23 914 | 光照变化 |
数据集 | 方法 估计 | 细化方式 | 范围 | 时间/s | 测试图像 | |||
---|---|---|---|---|---|---|---|---|
LM | DPOD | 41.15 | 37.3 | 45.0 | — | 局部 | 0.024 | RGB |
LM-O | DPOD | 11.35 | 10.1 | 12.6 | — | 局部 | 0.172 | RGB |
PVNet | 59.25 | 50.2 | 68.3 | ICP | 局部 | — | RGB-D | |
PVNet | 48.55 | 42.8 | 54.3 | — | 局部 | — | RGB | |
SSD-6D | 6.50 | 4.7 | 8.3 | — | 全局 | — | RGB | |
CDPN | 46.50 | 39.3 | 53.7 | — | 局部 | 0.279 | RGB | |
CDPNv2 | 57.90 | 46.9 | 68.9 | ICP | 局部 | 0.506 | RGB-D | |
Pix2Pose | 55.20 | 47.3 | 63.1 | — | 局部 | 5.195 | RGB-D | |
Pix2Pose | 27.00 | 23.3 | 30.7 | — | 局部 | 1.310 | RGB | |
Pix2Pose-PBR | 18.30 | 15.6 | 21.0 | — | 局部 | 1.157 | RGB | |
CosyPose-PBR | 65.75 | 56.7 | 74.8 | ICP | 全局 | 8.289 | RGB-D | |
CosyPose-PBR | 54.30 | 48.0 | 60.6 | — | 全局 | 0.550 | RGB | |
T-LESS | DPOD | 5.15 | 4.8 | 5.5 | — | 局部 | 0.206 | RGB |
CDPN | 39.75 | 37.7 | 41.8 | — | 局部 | 0.708 | RGB | |
CDPNv2 | 40.85 | 36.8 | 44.9 | ICP | 局部 | 2.486 | RGB-D | |
Pix2Pose | 27.85 | 26.1 | 29.6 | — | 局部 | 1.084 | RGB | |
Pix2Pose | 49.30 | 43.8 | 54.8 | ICP | 局部 | 4.180 | RGB-D | |
YCB-V | DPOD | 20.60 | 19.6 | 21.6 | — | 局部 | 0.341 | RGB |
CDPN | 40.35 | 32.9 | 47.8 | — | 局部 | 0.208 | RGB | |
CDPNv2-PBR | 55.70 | 51.1 | 60.3 | ICP | 局部 | 1.034 | RGB-D | |
CDPNv2 | 64.55 | 59.0 | 70.1 | ICP | 局部 | 0.637 | RGB-D | |
Pix2Pose | 40.05 | 37.2 | 42.9 | — | 局部 | 1.025 | RGB | |
Pix2Pose | 79.15 | 76.6 | 81.7 | ICP | 局部 | 2.590 | RGB-D | |
CosyPose | 80.70 | 77.2 | 84.2 | — | 全局 | 0.241 | RGB | |
CosyPose-PBR | 53.50 | 51.6 | 55.4 | — | 全局 | 0.342 | RGB | |
TUD-L | DPOD | 19.25 | 16.2 | 22.3 | — | 局部 | 0.046 | RGB |
CDPN | 69.95 | 60.5 | 79.4 | — | 局部 | 0.089 | RGB | |
CDPNv2 | 89.70 | 83.2 | 96.2 | ICP | 局部 | 0.313 | RGB-D | |
CDPNv2-PBR | 77.25 | 69.8 | 84.7 | ICP | 局部 | 0.280 | RGB-D | |
Pix2Pose | 30.95 | 25.5 | 36.4 | — | 局部 | 0.419 | RGB | |
Pix2Pose | 79.90 | 73.7 | 86.1 | ICP | 局部 | 1.336 | RGB-D | |
CosyPose | 74.80 | 68.9 | 80.7 | — | 全局 | 0.238 | RGB | |
CosyPose | 92.05 | 86.9 | 97.2 | ICP | 全局 | 0.812 | RGB-D |
Tab. 2 Comparison of training methods based RGB images in BOP
数据集 | 方法 估计 | 细化方式 | 范围 | 时间/s | 测试图像 | |||
---|---|---|---|---|---|---|---|---|
LM | DPOD | 41.15 | 37.3 | 45.0 | — | 局部 | 0.024 | RGB |
LM-O | DPOD | 11.35 | 10.1 | 12.6 | — | 局部 | 0.172 | RGB |
PVNet | 59.25 | 50.2 | 68.3 | ICP | 局部 | — | RGB-D | |
PVNet | 48.55 | 42.8 | 54.3 | — | 局部 | — | RGB | |
SSD-6D | 6.50 | 4.7 | 8.3 | — | 全局 | — | RGB | |
CDPN | 46.50 | 39.3 | 53.7 | — | 局部 | 0.279 | RGB | |
CDPNv2 | 57.90 | 46.9 | 68.9 | ICP | 局部 | 0.506 | RGB-D | |
Pix2Pose | 55.20 | 47.3 | 63.1 | — | 局部 | 5.195 | RGB-D | |
Pix2Pose | 27.00 | 23.3 | 30.7 | — | 局部 | 1.310 | RGB | |
Pix2Pose-PBR | 18.30 | 15.6 | 21.0 | — | 局部 | 1.157 | RGB | |
CosyPose-PBR | 65.75 | 56.7 | 74.8 | ICP | 全局 | 8.289 | RGB-D | |
CosyPose-PBR | 54.30 | 48.0 | 60.6 | — | 全局 | 0.550 | RGB | |
T-LESS | DPOD | 5.15 | 4.8 | 5.5 | — | 局部 | 0.206 | RGB |
CDPN | 39.75 | 37.7 | 41.8 | — | 局部 | 0.708 | RGB | |
CDPNv2 | 40.85 | 36.8 | 44.9 | ICP | 局部 | 2.486 | RGB-D | |
Pix2Pose | 27.85 | 26.1 | 29.6 | — | 局部 | 1.084 | RGB | |
Pix2Pose | 49.30 | 43.8 | 54.8 | ICP | 局部 | 4.180 | RGB-D | |
YCB-V | DPOD | 20.60 | 19.6 | 21.6 | — | 局部 | 0.341 | RGB |
CDPN | 40.35 | 32.9 | 47.8 | — | 局部 | 0.208 | RGB | |
CDPNv2-PBR | 55.70 | 51.1 | 60.3 | ICP | 局部 | 1.034 | RGB-D | |
CDPNv2 | 64.55 | 59.0 | 70.1 | ICP | 局部 | 0.637 | RGB-D | |
Pix2Pose | 40.05 | 37.2 | 42.9 | — | 局部 | 1.025 | RGB | |
Pix2Pose | 79.15 | 76.6 | 81.7 | ICP | 局部 | 2.590 | RGB-D | |
CosyPose | 80.70 | 77.2 | 84.2 | — | 全局 | 0.241 | RGB | |
CosyPose-PBR | 53.50 | 51.6 | 55.4 | — | 全局 | 0.342 | RGB | |
TUD-L | DPOD | 19.25 | 16.2 | 22.3 | — | 局部 | 0.046 | RGB |
CDPN | 69.95 | 60.5 | 79.4 | — | 局部 | 0.089 | RGB | |
CDPNv2 | 89.70 | 83.2 | 96.2 | ICP | 局部 | 0.313 | RGB-D | |
CDPNv2-PBR | 77.25 | 69.8 | 84.7 | ICP | 局部 | 0.280 | RGB-D | |
Pix2Pose | 30.95 | 25.5 | 36.4 | — | 局部 | 0.419 | RGB | |
Pix2Pose | 79.90 | 73.7 | 86.1 | ICP | 局部 | 1.336 | RGB-D | |
CosyPose | 74.80 | 68.9 | 80.7 | — | 全局 | 0.238 | RGB | |
CosyPose | 92.05 | 86.9 | 97.2 | ICP | 全局 | 0.812 | RGB-D |
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