Surface Structured Light Decoding Algorithm Based on Illumination Intensity

Jiaxuan Han; Yi Qian; Yang Yang; Shigang Liu

doi:10.3788/LOP241612

Laser & Optoelectronics Progress, Volume. 62, Issue 8, 0811004(2025)

Surface Structured Light Decoding Algorithm Based on Illumination Intensity

Jiaxuan Han^1,2、*, Yi Qian^1,3, Yang Yang², and Shigang Liu^1,2

¹Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, Shaanxi , China

²School of Computer Science, Shaanxi Normal University, Xi'an 710119, Shaanxi , China

³Basic Experimental Teaching Center, Shaanxi Normal University, Xi'an 710062, Shaanxi , China

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Figures & Tables(15)

Fig. 1. Encoding process of pixel point

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Fig. 2. Pixel decoding rules for different scenarios. (a) $d > g_{t o t a l}$ ; (b) $d \approx 0$ ; (c) $d \leq g_{t o t a l}$

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Fig. 3. Gray and inverse Gray codes

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Fig. 4. Schematic diagrams of pixel point that cannot be accurately decoded. (a) Project Gray code; (b) project inverse Gray code

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Fig. 5. Projected high-frequency pattern

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Fig. 6. Colored and white point clouds for toy model. (a) Toy model; (b) gray comparison algorithm; (c) algorithm in Ref. [16]; (d) proposed algorithm

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Fig. 7. Colored and white point clouds for remote control model. (a) Remote control model; (b) gray comparison algorithm; (c) algorithm in Ref. [16]; (d) proposed algorithm

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Fig. 8. Colored and white point clouds for flat model. (a) Flat model; (b) gray comparison algorithm; (c) algorithm in Ref. [16]; (d) proposed algorithm

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Fig. 9. Decoded pixel point quantities of three algorithms

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Fig. 10. Reconstructed pixel point quantities of three algorithms

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Fig. 11. Colored decoding results of three models after processing by different algorithms. (a) Toy model with smooth surface; (b) remote control model with complex surface; (c) flat model with flat surface

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Fig. 12. Variation in reconstructed pixel point quantities for different models with $b$ value. (a) Toy model with smooth surface; (b) remote control model with complex surface; (c) flat model with flat surface

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Fig. 13. Partial scenes of confirmatory experiments

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Table 1. System calibration parameters

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Table 1. System calibration parameters

Parameter

Camera

Projector

Internal parameter matrix

K_{c} = [\begin{matrix} 5904.6904 & 0 & 1339.3154 \\ 0 & 5899.0437 & 928.8375 \\ 0 & 0 & 1.0000 \end{matrix}]

K_{p} = [\begin{matrix} 1894.6016 & 0 & 443.1628 \\ 0 & 1886.2045 & 649.2692 \\ 0 & 0 & 1.0000 \end{matrix}]

External parameter matrix

$t = {[\begin{matrix} 400.898502674404 & - 148.073208916406 & - 254.3588193492409 \end{matrix}]}^{Τ}$

$R = [\begin{matrix} 0.9498827094490556 & 0.06184980437961044 & - 0.30642689175058530 \\ - 0.0449747890284132 & 0.99707255326942500 & 0.06183519926901993 \\ 0.3093543383267329 & - 0.04495470181186390 & 0.94988366032027080 \end{matrix}]$

Calibration error /pixel

0.146506

0.106845

Table 2. Reconstructed pixel point quantities under different b values in each scene
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Table 2. Reconstructed pixel point quantities under different b values in each scene
b Scene （a） Scene （b） Scene （c） Scene （d） Scene （e）
0.3 451278 442120 526689 357440 664212
0.4 495780 552235 564425 477432 724523
0.5 399275 421145 520241 339885 608845

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Jiaxuan Han, Yi Qian, Yang Yang, Shigang Liu. Surface Structured Light Decoding Algorithm Based on Illumination Intensity[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0811004

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