模型自适应的扫描视点自动规划

杨国庆; 王立忠; 任茂栋; 徐建宁; 赵建博; 王森; 李壮壮

doi:10.37188/CO.2024-0022

模型自适应的扫描视点自动规划

doi: 10.37188/CO.2024-0022

杨国庆^1,,
王立忠^{1, 2, ,},
任茂栋³,
徐建宁²,
赵建博²,
王森¹,
李壮壮²

1.
新疆大学机械工程学院, 新疆乌鲁木齐 830046
2.
西安交通大学机械工程学院机械制造系统工程国家重点实验室, 陕西西安 710049
3.
新拓三维技术(深圳)有限公司创新实验室, 广东深圳 518060)

基金项目: 国家重点研发计划项目（No. 2022YFB4601802）

详细信息

作者简介:
杨国庆（1999—），男，甘肃天水人，在读硕士研究生，2022年于新疆大学获得学士学位，主要从事三维光学测量技术的研究。E-mail：1260372224@qq.com

王立忠（1968—），男，山东梁山人，博士，教授，博士生导师，2004年于西安交通大学获得博士学位，主要从事三维光学测量技术的研究。E-mail：wanglz@mail.xjtu.edu.cn

中图分类号: TP391.4;O348.1
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出版历程
- 收稿日期: 2024-01-27
- 录用日期: 2024-04-15
- 网络出版日期: 2024-05-10

Model adaptive scanning viewpoint automatic planning

1.
College of Mechanical Engineering, Xinjiang University, Urumqi 830046, China
2.
State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3.
Innovation Lab, XTOP 3D Technology (Shenzhen) Co.Ltd., Shenzhen 518060, China)

Funds: Supported by

More Information

Corresponding author: wanglz@mail.xjtu.edu.cn

摘要

摘要:
当进行扫描重建时，示教扫描繁琐且通用性差，目前视点规划的重点依然是自动获取覆盖模型的最少视点集。本文为了实现对不同复杂程度零部件的自动化三维扫描重建，对视点规划过程中可能发生的视点冗余、视点遮挡、双目重建约束等问题进行研究。首先，针对现有视点规划难以对模型完整扫描的问题，通过分析面结构光扫描的特性，对Lloyd’s算法进行改进，提出使用欧式距离和法向量偏差的能量函数对模型进行Voronoi划分，生成初始扫描视点。接着，针对视点冗余问题，提出了对初始扫描视点进行分裂的迭代算法。最后，针对生成的视点容易产生遮挡的问题，提出了一种视线去遮挡策略，并以提高模型覆盖率为目的，提出了使用追扫视点的方法。实验结果表明：在最佳视点数量下，对于汽车铸件和壳体的覆盖率达到了94%以上，对于简单曲面汽车钣金的覆盖率达到了99.5%以上，并实现了汽车转向节的自动规划扫描，满足视点自动规划的覆盖率和效率以及对不同复杂程度零件的适应性要求。
- 视点规划 /
- Voronoi划分 /
- 视点冗余 /
- 视点遮挡 /
- 双目约束
Abstract:
The teaching scan is cumbersome and has poor versatility when performing scan reconstruction. The current focus of viewpoint planning is still to automatically obtain the minimum set of viewpoints covering the model. To realize automated 3D scanning and reconstruction of parts of different complexity levels, this paper studies issues such as viewpoint redundancy, viewpoint occlusion, and binocular reconstruction constraints that may occur during viewpoint planning. First, given the problem that it is difficult to completely scan the model with existing viewpoint planning, Lloyd's algorithm was improved by analyzing the characteristics of surface structured light scanning and proposed using the energy function of Euclidean distance and normal vector deviation to perform Voronoi partitioning of the model to generate Initial scanning viewpoint. Then, to address the viewpoint redundancy problem, an iterative algorithm for splitting the initial scanning viewpoints is proposed. Finally, given the problem that the generated viewpoints are prone to occlusion, a line-of-sight de-occlusion strategy is proposed, and to improve the model coverage, a method of using panning viewpoints is proposed. The experimental results show that: under the optimal number of viewpoints, the coverage rate of automobile castings and transmission housing reaches more than 94%, the coverage rate of simple curved automobile sheet metal reaches more than 99.5%, and the automatic steering knuckle of the automobile is realized. Planning scanning meets the coverage and efficiency requirements of automatic viewpoint planning and the adaptability requirements for parts of different complexity.
- viewpoint planning /
- Voronoi division /
- viewpoint redundancy /
- viewpoint occlusion /
- binocular constraints

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图 1 面结构光扫描仪

Figure 1. Surface structured light scanner

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图 2 单目相机景深

Figure 2. Monocular camera depth of field

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图 3 扫描仪可视区域示意图

Figure 3. Scanner visual area schematic diagram

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图 4 质心投影

Figure 4. Centroid projection

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图 5 混合Voronoi面积

Figure 5. Mixed Voronoi area

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图 6 模型分割算法流程图

Figure 6. Model segmentation algorithm flow chart

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图 7 视线可达性

Figure 7. Line-of-sight accessibility

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图 8 去冗余算法流程图

Figure 8. De-redundancy algorithm flow chart

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图 9 模型截面下的视线移动策略

Figure 9. Line-of-sight movement strategy under model sectiont

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图 10 三维空间中的视线移动策略

Figure 10. Line-of-sight movement strategy in 3D space

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图 11 双目相机旋转策略

Figure 11. Binocular camera rotation strategy

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图 12 去遮挡算法流程图

Figure 12. De-occlusion algorithm flow chart

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图 13 自动化测量系统

Figure 13. Automatic measurement system

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图 14 零件实物图

Figure 14. Parts physical drawing

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图 15 零件网格模型

Figure 15. Parts mesh model

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图 16 壳体视点规划示意图

Figure 16. Transmission housing viewpoint planning diagram

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图 17 铸件视点规划示意图

Figure 17. Casting viewpoint planning diagram

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图 18 钣金1视点规划示意图

Figure 18. Sheet metal 1 viewpoint planning diagram

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图 19 钣金2视点规划示意图

Figure 19. Sheet metal 2 viewpoint planning diagram

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图 20 铸件曲率示意图

Figure 20. Casting curvature diagram

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图 21 转向节模型

Figure 21. Steering knuckle model

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图 22 转向节视点规划示意图

Figure 22. Steering knuckle viewpoint planning diagram

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图 23 转向节扫描实验图

Figure 23. Steering knuckle scanning experiment

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表 1 汽车壳体扫描结果

Table 1. Scannin result of Automobile transmission housing

$ \alpha $权值	视点数量	处理耗时	覆盖率	追加视点数量	处理耗时	覆盖率（追扫）
0.1	77	167.69	91.64	20	14.53	93.41
0.1	77	157.28	91.11	20	14.40	93.41
0.1	79	183.77	92.07	18	11.19	93.53
0.15	158	324.06	94.17	43	22.62	95.39
0.15	156	363.92	94.90	41	23.07	95.80
0.15	161	290.06	94.17	39	17.81	95.33
0.2	237	481.22	95.60	75	47.89	96.33
0.2	245	538.25	95.90	72	27.67	96.53
0.2	237	476.17	95.60	66	43.08	96.41

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表 2 汽车铸件扫描结果

Table 2. Scanning results of automobile castings

$ \alpha $权值	视点数量	处理耗时	覆盖率	追加视点数量	处理耗时	覆盖率（追扫）
0.1	49	180.83	93.39	14	13.86	94.68
0.1	44	157.02	91.60	14	17.63	94.19
0.1	50	175.53	92.05	14	11.62	94.88
0.15	110	342.40	94.76	33	20.21	96.39
0.15	100	309.02	95.67	32	20.05	97.21
0.15	110	319.99	94.80	35	23.27	96.46
0.2	166	392.54	96.97	50	36.33	97.84
0.2	181	504.09	96.83	57	36.74	97.80
0.2	173	466.90	96.90	58	40.29	98.04

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表 3 钣金1扫描结果

Table 3. Sheet metal 1 scan results

$ \alpha $权值	视点数量	处理耗时	覆盖率	追加视点数量	处理耗时	覆盖率（追扫）
0.0075	49	31.36	98.41	5	0.55	98.41
0.0075	49	33.48	98.66	6	0.53	98.66
0.0075	43	36.05	96.37	5	0.45	96.37
0.01	51	35.97	99.52	5	0.50	99.52
0.01	56	41.36	99.52	2	0.18	99.53
0.01	53	33.11	99.52	5	0.67	99.53
0.0125	63	42.03	99.67	4	0.38	99.67
0.0125	62	39.45	99.34	6	0.49	99.34
0.0125	63	46.74	99.67	4	0.37	99.67

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表 4 钣金2扫描结果

Table 4. Sheet metal 2 scan results

$ \alpha $权值	视点数量	处理耗时	覆盖率	追加视点数量	处理耗时	覆盖率（追扫）
0.01	23	29.95	97.95	5	4.42	97.97
0.01	22	24.20	97.94	3	2.18	97.94
0.01	28	29.81	99.68	5	3.27	99.69
0.0125	30	32.50	99.93	7	3.32	99.94
0.0125	30	38.59	99.92	6	2.44	99.94
0.0125	30	35.12	99.93	7	3.41	99.94
0.015	29	33.77	99.93	7	3.39	99.94
0.015	34	35.44	99.92	9	3.44	99.95
0.015	29	33.21	99.93	7	3.40	99.94

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