Adaptive 3D Mesh Model Reconstruction Based on Industrial CT Images

Ling Li; Heng Jin; Jie Liu; Chao Long; Yunyong He; Zhongming Li; Liming Duan

doi:10.3788/AOS221477

Journals >Acta Optica Sinica >Volume 43 >Issue 3 >Page 0334001 > Article

Acta Optica Sinica
Vol. 43, Issue 3, 0334001 (2023)

Adaptive 3D Mesh Model Reconstruction Based on Industrial CT Images

Ling Li^1、2, Heng Jin^1、2, Jie Liu^1、3, Chao Long^1、3, Yunyong He^1、3, Zhongming Li^1、2, and Liming Duan^1、2、*

Author Affiliations

¹ICT Research Center, Key Laboratory of Optoelectronic Technology & Systems, Ministry of Education, Chongqing University, Chongqing 400044, China

²College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China

³College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China

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DOI: 10.3788/AOS221477 Cite this Article Set citation alerts

Ling Li, Heng Jin, Jie Liu, Chao Long, Yunyong He, Zhongming Li, Liming Duan. Adaptive 3D Mesh Model Reconstruction Based on Industrial CT Images[J]. Acta Optica Sinica, 2023, 43(3): 0334001 Copy Citation Text

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Results of image denoising by different algorithms. (a) Original image; (b) mean filtering; (c) Gaussian filtering; (d) median filtering; (e) nonlocal mean filtering; (f) bilateral filtering

Fig. 1. Results of image denoising by different algorithms. (a) Original image; (b) mean filtering; (c) Gaussian filtering; (d) median filtering; (e) nonlocal mean filtering; (f) bilateral filtering

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Fig. 2. Schematic of a voxel

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Fig. 3. Flow chart for creating an octree

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Fig. 4. Location of feature point. (a) Feature point is outside the voxel; (b) feature point is inside the voxel

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Fig. 5. Connection mode of quadrilateral

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Fig. 6. Mode of dividing quadrilateral

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Fig. 7. Reconstruction results of different algorithms. (a) MC algorithm; (b) proposed algorithm

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Fig. 8. Using different simplification algorithms to simplify the gear model. (a) Mesh model; (b) algorithm of Ref. [9]; (c) algorithm of Ref. [12]; (d) proposed algorithm

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Fig. 9. Engine models under different simplified parameters. (a) Unsimplified model (

φ = - 0.01

); (b) simplified model 1 (

φ = 0.01

); (c) simplified model 2 (

φ = 0.1

); (d) simplified model 3 (

φ = 1

); (e) simplified model 4 (

φ = 10

); (f) simplified model 5 (

φ = 100

)

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Simplified parameter $φ$	Proportion of internal node / %	Proportion of leaf node / %	Proportion of pseudo leaf node /%
-0.01	24.81	75.19	0
0.01	24.15	71.49	4.36
0.1	24.14	71.33	4.53
1	23.20	65.80	11.00
10	20.58	0.24	79.18
100	20.41	0	79.59

Table 1. Proportion of each type of node under different simplified parameters

Model	Simplified parameter $φ$	Simplification rate /%	Mesh quality /%
Gear	-0.01	0	100.00
	0.01	42.90	99.77
	0.1	43.20	99.76
	1	60.36	99.57
	10	90.09	99.17
	100	96.74	99.05
Engine	-0.01	0	100.00
	0.01	15.59	99.95
	0.1	16.58	99.94
	1	31.71	99.90
	10	79.68	99.60
	100	92.03	99.56

Table 2. Performance of proposed algorithm with different parameters

Ling Li, Heng Jin, Jie Liu, Chao Long, Yunyong He, Zhongming Li, Liming Duan. Adaptive 3D Mesh Model Reconstruction Based on Industrial CT Images[J]. Acta Optica Sinica, 2023, 43(3): 0334001

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