Multi-depth-of-field 3D shape reconstruction with global spatio-temporal feature coupling

doi:10.11772/j.issn.1001-9081.2022101589

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 894-902.DOI: 10.11772/j.issn.1001-9081.2022101589

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles Next Articles

Multi-depth-of-field 3D shape reconstruction with global spatio-temporal feature coupling

Jiangfeng ZHANG¹^,², Tao YAN¹^,²^,³^,⁴(), Bin CHEN⁴^,⁵, Yuhua QIAN²^,³, Yantao SONG¹^,²^,³

^1.School of Computer and Information Technology，Shanxi University，Taiyuan Shanxi 030006，China
^2.Institute of Big Data Science and Industry，Shanxi University，Taiyuan Shanxi 030006，China
^3.Engineering Research Center for Machine Vision and Data Mining of Shanxi Province （Shanxi University），Taiyuan Shanxi 030006，China
^4.Chongqing Research Institute of Harbin Institute of Technology，Chongqing 401151，China
^5.International Research Institute for Artificial Intelligence，Harbin Institute of Technology （Shenzhen），Shenzhen Guangdong 518055，China

Received:2022-10-25 Revised:2023-01-12 Accepted:2023-01-16 Online:2023-03-15 Published:2023-03-10
Contact: Tao YAN
About author:ZHANG Jiangfeng， born in 1998， M. S. candidate. His research interests include deep learning， 3D reconstruction.
CHEN Bin， born in 1970， Ph. D.， professor. His research interests include computer vision.intelligence， machine learning.
QIAN Yuhua， born in 1976， Ph. D.， professor. His researchinterests include artificial intelligence， machine learning.
SONG Yantao， born in 1989， Ph. D.， associate professor. Her research interests include medical image processing.
Supported by:
National Natural Science Foundation of China(62006146);Fundamental Research Program of Shanxi Province(201901D211170)

全局时空特征耦合的多景深三维形貌重建

张江峰¹^,², 闫涛¹^,²^,³^,⁴(), 陈斌⁴^,⁵, 钱宇华²^,³, 宋艳涛¹^,²^,³

^1.山西大学计算机与信息技术学院, 太原 030006
^2.山西大学大数据科学与产业研究院, 太原 030006
^3.山西省机器视觉与数据挖掘工程研究中心(山西大学), 太原 030006
^4.哈尔滨工业大学重庆研究院, 重庆 401151
^5.哈尔滨工业大学(深圳) 国际人工智能研究院, 广东深圳 518055

通讯作者: 闫涛
作者简介:张江峰（1998—），男，山西晋城人，硕士研究生，CCF会员，主要研究方向：深度学习、三维重建
闫涛（1987—），男，山西定襄人，副教授，博士，CCF会员，主要研究方向：三维重建
陈斌（1970—），男，四川广汉人，教授，博士，主要研究方向：机器视觉
钱宇华（1976—），男，山西晋城人，教授，博士，CCF会员，主要研究方向：人工智能、机器学习
宋艳涛（1989—），女，山西临汾人，副教授，博士，主要研究方向：医学图像处理。
基金资助:
国家自然科学基金资助项目(62006146);山西省基础研究计划资助项目(201901D211170)

Abstract

Abstract:

In response to the inability of existing 3D shape reconstruction models to effectively fuse global spatio-temporal information， a Depth Focus Volume （DFV） module was proposed to retain the transition information of focus and defocus， on this basis， a Global Spatio-Temporal Feature Coupling （GSTFC） model was proposed to extract local and global spatio-temporal feature information of multi-depth-of-field image sequences. Firstly， the 3D-ConvNeXt module and 3D convolutional layer were interspersed in the shrinkage path to capture multi-scale local spatio-temporal features. Meanwhile， the 3D-SwinTransformer module was added to the bottleneck module to capture the global correlations of local spatio-temporal features of multi-depth-of-field image sequences. Then， the local spatio-temporal features and global correlations were fused into global spatio-temporal features through the adaptive parameter layer， which were input into the expansion path to guide and generate focus volume. Finally， the sequence weight information of the focus volume was extracted by DFV and the transition information of focus and defocus was retained to obtain the final depth map. Experimental results show that GSTFC decreases the Root Mean Square Error （RMSE） index by 12.5% on FoD500 dataset compared with the state-of-the-art All-in-Focus Depth Net （AiFDepthNet） model， and retains more depth-of-field transition relationships compared with the traditional Robust Focus Volume Regularization in Shape from Focus （RFVR-SFF） model.

Key words: 3D shape reconstruction, deep learning, supervised learning, spatio-temporal feature coupling, depth map

摘要：

针对现有三维形貌重建模型无法有效融合全局时空信息的问题，设计深度聚焦体积（DFV）模块保留聚焦和离焦的过渡信息，并在此基础上提出全局时空特征耦合（GSTFC）模型提取多景深图像序列的局部与全局的时空特征信息。首先，在收缩路径中穿插3D-ConvNeXt模块和3D卷积层，捕捉多尺度局部时空特征，同时，在瓶颈模块中添加3D-SwinTransformer模块捕捉多景深图像序列局部时序特征的全局关联关系；然后，通过自适应参数层将局部时空特征和全局关联关系融合为全局时空特征，并输入扩张路径引导生成聚焦体积；最后，聚焦体积通过DFV提取序列权重信息，并保留聚焦与离焦的过渡信息，得到最终深度图。实验结果表明，GSTFC在FoD500数据集上的均方根误差（RMSE）相较于最先进的全聚焦深度网络（AiFDepthNet）下降了12.5%，并且比传统的鲁棒聚焦体积正则化的聚焦形貌恢复（RFVR-SFF）模型保留了更多的景深过渡关系。

关键词: 三维形貌重建, 深度学习, 有监督学习, 时空特征耦合, 深度图

CLC Number:

TP391.41

Jiangfeng ZHANG, Tao YAN, Bin CHEN, Yuhua QIAN, Yantao SONG. Multi-depth-of-field 3D shape reconstruction with global spatio-temporal feature coupling[J]. Journal of Computer Applications, 2023, 43(3): 894-902.

张江峰, 闫涛, 陈斌, 钱宇华, 宋艳涛. 全局时空特征耦合的多景深三维形貌重建[J]. 《计算机应用》唯一官方网站, 2023, 43(3): 894-902.

Figures/Tables 8

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模型	主干	模块	特征处理	MSE	参数量/10⁶	浮点运算量/GFLOPs
U	3D-U型主干	—	卷积层	0.004 23	29.160 0	534.27
U+T	3D-U型主干	3D-SwinTransformer	卷积层	0.003 73	35.657 0	549.18
U+T+DFV	3D-U型主干	3D-SwinTransformer	DFV	0.003 62	35.660 0	549.18
X	3D-ConvNeXt	—	卷积层	0.005 10	9.760 0	233.17
X+T	3D-ConvNeXt	3D-SwinTransformer	卷积层	0.002 21	16.251 1	248.09
X+T+DFV	3D-ConvNeXt	3D-SwinTransformer	DFV	0.000 98	16.251 0	248.08

模型	主干	模块	特征处理	MSE	参数量/10⁶	浮点运算量/GFLOPs
U	3D-U型主干	—	卷积层	0.004 23	29.160 0	534.27
U+T	3D-U型主干	3D-SwinTransformer	卷积层	0.003 73	35.657 0	549.18
U+T+DFV	3D-U型主干	3D-SwinTransformer	DFV	0.003 62	35.660 0	549.18
X	3D-ConvNeXt	—	卷积层	0.005 10	9.760 0	233.17
X+T	3D-ConvNeXt	3D-SwinTransformer	卷积层	0.002 21	16.251 1	248.09
X+T+DFV	3D-ConvNeXt	3D-SwinTransformer	DFV	0.000 98	16.251 0	248.08

模型	MSE	RMSE	Sqr.rel	Bumpiness	参数量/10³
RDF	0.111 5	0.322	0.239 5	1.54	—
DDFF	0.033 4	0.167	0.035 6	1.74	39 806 222
DefocusNet	0.021 8	0.134	0.035 9	2.52	1 508 047
AiFDepthNet	0.012 7	0.104	—	—	16 533 873
FV-Net	0.018 8	0.125	0.024 3	1.45	15 963 225
DFV-Net	0.020 5	0.129	0.023 9	1.43	—
GSTFC	0.009 8	0.091	0.057 5	0.51	16 269 231

模型	MSE	RMSE	Sqr.rel	Bumpiness	参数量/10³
RDF	0.111 5	0.322	0.239 5	1.54	—
DDFF	0.033 4	0.167	0.035 6	1.74	39 806 222
DefocusNet	0.021 8	0.134	0.035 9	2.52	1 508 047
AiFDepthNet	0.012 7	0.104	—	—	16 533 873
FV-Net	0.018 8	0.125	0.024 3	1.45	15 963 225
DFV-Net	0.020 5	0.129	0.023 9	1.43	—
GSTFC	0.009 8	0.091	0.057 5	0.51	16 269 231

数据集	模型	RMSE	PSNR/dB	SSIM	Correlation
Base-Line	SF	0.032 6	30.100 6	0.947 5	0.946 5
	TENV	0.020 8	33.889 6	0.935 4	0.994 4
	DLAP	0.025 3	32.900 6	0.910 2	0.996 0
	FDC	0.092 4	20.875 7	0.649 6	0.495 4
	GC	0.122 0	18.653 7	0.676 8	0.779 6
	RDF	0.024 7	32.847 8	0.904 8	0.962 4
	RFVR-SFF	0.008 0	42.813 1	0.952 3	0.996 9
	GSTFC	0.005 8	45.714 2	0.967 6	0.998 2
4D Light Field	SF	0.040 7	28.630 6	0.899 0	0.904 2
	TENV	0.042 9	28.397 2	0.887 5	0.882 5
	DLAP	0.030 9	31.297 5	0.920 8	0.945 6
	FDC	0.088 8	21.203 1	0.563 8	0.523 9
	GC	0.112 9	19.162 5	0.796 0	0.697 4
	RDF	0.055 7	25.934 5	0.871 3	0.816 5
	RFVR-SFF	0.029 9	32.096 7	0.895 0	0.934 6
	GSTFC	0.026 2	33.686 5	0.921 7	0.947 6
POV-Ray	SF	0.099 0	20.165 9	0.611 7	0.731 4
	TENV	0.099 8	20.249 0	0.614 7	0.704 3
	DLAP	0.077 1	22.361 1	0.658 4	0.835 9
	FDC	0.123 4	18.228 6	0.465 4	0.508 7
	GC	0.140 3	17.086 7	0.532 1	0.546 2
	RDF	0.112 2	19.038 8	0.604 4	0.666 1
	RFVR-SFF	0.093 6	20.802 0	0.500 2	0.771 8
	GSTFC	0.076 6	22.402 4	0.655 0	0.845 1
SLFD and DLFD	SF	0.047 8	26.754 0	0.888 8	0.904 5
	TENV	0.056 0	25.628 3	0.857 9	0.859 1
	DLAP	0.036 1	29.377 6	0.919 2	0.944 9
	FDC	0.101 4	20.026 1	0.541 3	0.553 6
	GC	0.125 9	18.178 3	0.776 9	0.710 7
	RDF	0.066 1	24.223 2	0.869 7	0.807 4
	RFVR-SFF	0.085 5	21.720 0	0.388 8	0.726 4
	GSTFC	0.038 7	28.987 6	0.914 5	0.927 4

Multi-depth-of-field 3D shape reconstruction with global spatio-temporal feature coupling

全局时空特征耦合的多景深三维形貌重建

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[1]	Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877.
[2]	Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718.
[3]	Yunchuan HUANG, Yongquan JIANG, Juntao HUANG, Yan YANG. Molecular toxicity prediction based on meta graph isomorphism network [J]. Journal of Computer Applications, 2024, 44(9): 2964-2969.
[4]	Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
[5]	Jieru JIA, Jianchao YANG, Shuorui ZHANG, Tao YAN, Bin CHEN. Unsupervised person re-identification based on self-distilled vision Transformer [J]. Journal of Computer Applications, 2024, 44(9): 2893-2902.
[6]	Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
[7]	Xiyuan WANG, Zhancheng ZHANG, Shaokang XU, Baocheng ZHANG, Xiaoqing LUO, Fuyuan HU. Unsupervised cross-domain transfer network for 3D/2D registration in surgical navigation [J]. Journal of Computer Applications, 2024, 44(9): 2911-2918.
[8]	Yingjun ZHANG, Niuniu LI, Binhong XIE, Rui ZHANG, Wangdong LU. Semi-supervised object detection framework guided by curriculum learning [J]. Journal of Computer Applications, 2024, 44(8): 2326-2333.
[9]	Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557.
[10]	Yanjie GU, Yingjun ZHANG, Xiaoqian LIU, Wei ZHOU, Wei SUN. Traffic flow forecasting via spatial-temporal multi-graph fusion [J]. Journal of Computer Applications, 2024, 44(8): 2618-2625.
[11]	Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction [J]. Journal of Computer Applications, 2024, 44(8): 2643-2650.
[12]	Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks [J]. Journal of Computer Applications, 2024, 44(7): 2310-2318.
[13]	Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199.
[14]	Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215.
[15]	Zheng WU, Zhiyou CHENG, Zhentian WANG, Chuanjian WANG, Sheng WANG, Hui XU. Deep learning-based classification of head movement amplitude during patient anaesthesia resuscitation [J]. Journal of Computer Applications, 2024, 44(7): 2258-2263.