基于轻型自限制注意力的结构光相位及深度估计混合网络

朱新军; 赵浩淼; 王红一; 宋丽梅; 孙瑞群

doi:10.37188/CO.2023-0066

基于轻型自限制注意力的结构光相位及深度估计混合网络

doi: 10.37188/CO.2023-0066

天津工业大学人工智能学院, 天津 300387

基金项目: 国家自然科学基金 (No. 61905178)；天津市教委科研计划项目 (No. 2019KJ021)

详细信息

作者简介:
朱新军（1985—），男，山东临沂人，博士，副教授，硕士生导师，2008年于临沂师范学院获得学士学位, 2011年于山东理工大学获得硕士学位，2015年于天津大学获得博士学位，主要从事光学三维测量与智能计算成像的研究。E-mail：xinjunzhu@tiangong.edu.cn

中图分类号: TP394.1;TH691.9
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出版历程
- 收稿日期: 2023-04-14
- 修回日期: 2023-05-15
- 网络出版日期: 2023-09-18

A hybrid network based on light self-limited attention for structured light phase and depth estimation

School of Artificial Intelligence, Tiangong University, Tianjin 300387, China

Funds: Supported by National Natural Science Foundation of China (No. 61905178); Science & Technology Development Fund of Tianjin Education Commission for Higher Education (No. 2019KJ021)

More Information

Corresponding author: xinjunzhu@tiangong.edu.cn

摘要

摘要:
相位提取与深度估计是结构光三维测量中的重点环节，目前传统方法在结构光相位提取与深度估计方面存在效率不高、结果不够鲁棒等问题。为了提高深度学习结构光的重建效果，本文提出了一种基于轻型自限制注意力（Light Self-Limited-Attention，LSLA）的结构光相位及深度估计混合网络，即构建一种CNN-Transformer的混合模块，并将构建的混合模块放入U型架构中，实现CNN与Transformer的优势互补。将所提出的网络在结构光相位估计和结构光深度估计两个任务上进行实验，并和其他网络进行对比。实验结果表明：相比其他网络，本文所提出的网络在相位估计和深度估计的细节处理上更加精细，在结构光相位估计实验中，精度最高提升31%；在结构光深度估计实验中，精度最高提升26%。该方法提高了深度神经网络在结构光相位估计及深度估计的准确性。
- 结构光 /
- 深度学习 /
- 自限制注意力 /
- 相位估计 /
- 深度估计
Abstract:
Phase retrieval and depth estimation are vital to three-dimensional measurement using structured light. Currently, conventional methods for structured light phase retrieval and depth estimation have limited efficiency and are lack of robustness in their results and so on. To improve the reconstruction effect of structured light by deep learning, we propose a hybrid network for structured light phase and depth estimation based on Light Self-Limited Attention (LSLA). Specifically, a CNN-Transformer hybrid module is constructed and integrated into a U-shaped structure to realize the advantages complementary of CNN and Transformer. The proposed network is experimentally compared with other networks in structured light phase estimation and structured light depth estimation. The experimental results indicate that the proposed network achieves finer detail processing in phase and depth estimation compared to other networks. Specifically, for structured light phase and depth estimation, its accuracy improves by 31% and 26%, respectively. Therefore, the proposed network improves the accuracy of deep neural networks in the structured light phase and depth estimation areas.
- structured light /
- deep learning /
- self-limited attention /
- phase estimation /
- depth estimation

HTML全文

图 1 FPP系统原理图

Figure 1. Schematic diagram of the FPP system

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图 2 网络结构图

Figure 2. Network structure diagram

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图 3 CNN-Transformer模块结构图

Figure 3. Structure of the CNN-Transformer module

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图 4 部分数据示例图。第一行为仿真数据，第二行为真实数据。(a)仿真条纹图；(b)仿真条纹图D；(c)仿真条纹图M；(d)仿真条纹图包裹相位；(e)真实条纹图；(f)真实条纹图D；(g)真实条纹图M；(h)真实条纹图包裹相位

Figure 4. Sample maps in some datasets. The first lines are simulation data, the second lines are real data. (a) Simulation fringe map; (b) simulation fringe map D; (c) simulation fringe map M; (d) simulation fringe wrapped phase; (e) real fringe map; (f) real fringe map D; (g) real fringe map M; (h) real fringe wrapped phase

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图 5 不同网络仿真和真实数据包裹相位对比。蓝色框为仿真数据，橙色框为真实数据。(a)UNet；(b)DPH；(c)R2UNet；(d)SUNet；(e)Ours；(f)标签

Figure 5. Comparison of different network simulation and real data wrapped phases. The blue boxes are the simulation data, and the orange boxes are the real data. (a) UNet; (b) DPH; (c) R2UNet; (d) SUNet; (e) Ours; (f) Label

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图 6 包裹相位结果曲线图。(a)仿真数据结果比较；(b)真实数据结果比较

Figure 6. Wrapped phase curves.(a) Comparison of simulation data; (b) comparison of real data

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图 7 生成数据集流程图。(a) 模型导入；(b) 调整大小；(c) 投影条纹

Figure 7. Flowchart of dataset generation. (a) Model import; (b) adjust of the model size; (c) projection fringe

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图 8 部分数据示例图。(a)仿真条纹图；(b)真实条纹图；(c)仿真深度图；(d)真实深度图

Figure 8. Sample maps in the dataset. (a) Simulated fringe map; (b) real fringe map; (c) simulation depth map; (d) real depth map

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图 9 不同方法深度估计视觉结果比较。蓝色框为仿真数据，橙色框为真实数据。(a) 输入数据； (b) UNet；(c) DPH；(d) R2UNet；(e) Ours；(f)标签

Figure 9. Comparison of the visual results of depth estimation by different methods. The blue boxes are the simulation data, and the orange boxes are the real data. (a) Input data; (b) UNet; (c) DPH; (d) R2UNet; (e) Ours; (f) Label

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表 1 不同包裹相位计算方法比较

Table 1. Comparison of the different wrapped phase calculation methods

	MSE	时间t/s
直接预测包裹相位	0.2833	5.89
分别预测DM	0.1739	11.7
同时预测 DM	0.16806	7.54

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表 2 包裹相位预测方法比较

Table 2. Comparison of the wrapped phase prediction methods

	仿真数据		真实数据
	MSE	时间t/s	MSE	时间t/s
UNet	0.02658	6.67	0.16806	7.54
DPH	0.02710	11.65	0.12974	11.78
R2UNet	0.02734	13.69	0.12905	14.30
SUNet	0.02717	7.95	0.14350	8.29
Ours	0.02395	11.06	0.11622	11.67

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表 3 消融实验结果比较

Table 3. Comparison of ablation experiment results

	MSE	时间t/s
CMT	11.32	6.89
CMT替换LSLA	9.17	6.45
CMT替换FFN	11.34	5.54
CMT+U形结构	8.94	9.68

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表 4 不同方法深度估计结果比较

Table 4. Comparison of the depth estimation results by different methods

	仿真数据		真实数据
	MSE	时间t/s	MSE	时间t/s
Unet	8.78	5.98	9.97	6.44
DPH	8.03	8.66	9.86	10.59
R2UNet	7.57	8.73	8.72	10.92
Ours	6.43	8.09	7.64	8.44

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