Simulasi dan Eksperimen Profilometer Optik Tiga-Dimensi (3-D) Menggunakan Transformasi Fourier
DOI:
https://doi.org/10.26418/positron.v15i2.92893Keywords:
Citra 3-D, Eksperimen, Optik, Profilometer, Simulasi, Transformasi FourierAbstract
Pengetahuan bentuk tiga dimensi (3-D) permukaan obyek riil dengan menggunakan pencitraan optik sangat penting untuk kepentingan teknologi modern. Saat ini penggunaan kamera digital terbatas hanya mampu menghasilkan citra 2-D, sehingga kehilangan informasi penting terkait dengan bentuk asli obyek riil. Perangkat optik 3-D komersial yang ada masih mengandung banyak kelemahan. Di antara semua perangkat tersebut, profilometer optik 3-D berdasarkan pada structured light measurement (SLM) dengan algoritma phase-shifting profilometry (PSP) paling menarik karena menawarkan akurasi tinggi, kecepatan, dan efisiensi biaya. Namun, proses PSP membutuhkan banyak tangkapan citra (multi-frames) sehingga sangat rentan terhadap gerak obyek dan fluktuasi cahaya. Di sisi lain, profilometer optik 3-D Fourier Transform Profilometry (FTP) membutuhkan hanya satu citra (single frame) sehingga menawarkan metode SLM alternatif yang lebih sederhana, efisien, dan ekonomis. Akan tetapi, sejauh ini belum ada perangkat profilometer optik 3-D komersial yang bekerja berdasarkan pada FTP dikarenakan masih menghadapi beberapa kendala. Oleh karena itu, makalah ini menyajikan simulasi dan eksperimen untuk menguji kinerja FTP dan mengidentifikasi beberapa kendala yang muncul. Secara teknis FTP menggunakan perangkat proyektor Liquid Crystal Device (LCD) dan kamera digital. Proyektor LCD mengiluminasikan pola-pola frinji mirip sinyal sinusoidal ke atas permukaan obyek yang diuji. Kamera digital menangkap citra perubahan pola-pola frinji ini sebagai citra distorsi relatif terhadap citra referensi. Kedua citra diolah secara digital dengan algoritma Fast Fourier Transform (FFT) untuk mengekstraksi beda-fase sebagai dasar untuk merekonstruksi citra 3-D obyek riil yang diuji. Secara umum, hasil simulasi dan eksperimen menunjukkan bahwa FTP mampu menghasilkan rekonstruksi citra 3-D, baik permukaan obyek simulasi maupun obyek riil sesuai dengan bentuk asli obyek yang diuji. Hasil rekonstruksi citra 3-D masih memerlukan optimasi lebih lanjut. Namun demikian, hasil ini memberikan bukti awal (proof-of-concept) yang signifikan dan dapat menjadi dasar pengembangan profilometer optik 3-D yang aplikatif, misalnya untuk dokumentasi citra digital 3-D seni topeng atau artefak budaya lainnya.References
Kamal S. N. dan Ibrahim A. A., 3D Model Visualization Function for Responsive Web Design, Iraqi J. Comput. Sci. Math., 4 (4) pp. 76–91, 2023.
Mineo C., Cerniglia D., Ricotta V., and Reitinger B., Autonomous 3D Geometry Reconstruction Through Robot-Manipulated Optical Sensors,” Int. J. Adv. Manuf. Technol., 116 (5–6) pp. 1895–1911, 2021.
Wang Z., Yi R., Wen X., Zhu C., and Xu K., Cardiovascular medical image and analysis based on 3D vision: A comprehensive survey, Meta-Radiology, 2(4), p. 100102, 2024.
Zhou L., Fan M., Hansen C., Johnson C. R., and Weiskopf D., A Review of Three-Dimensional Medical Image Visualization, Heal. Data Sci., 2022(2022).
Cianci M. G. and Colaceci S., Laser Scanner and Uav for the 2D and 3D Reconstructions of Cultural Heritage, Scires-It, 12 (2), pp. 43–54, 2022.
Kewuyemi Y. O., Kesa H., and Adebo O. A., Trends in Functional Food Development with Three-dimensional (3D) Food Printing Technology: Prospects for Value-added Traditionally Processed Food Products, Crit. Rev. Food Sci. Nutr., 62 (28), pp. 7866–7904, 2022.
Lee D. K. and Park B. Y., A case study for 3D scanning-based quantitative quality control during key stages of composite small craft production, Int. J. Nav. Archit. Ocean Eng., 15, p. 100534, 2023.
Spagnolo G. S., Cozzella L., and Leccese F., Projected fringes profilometry for cultural heritage studies, 2019 IMEKO TC4 Int. Conf. Metrol. Archaeol. Cult. Heritage, MetroArchaeo 2019, pp. 435–438, 2019.
Al-Temeemy A. A. and Al-Saqal S. A., Laser-based structured light technique for 3D reconstruction using extreme laser stripes extraction method with global information extraction, Opt. Laser Technol., 138 (no. December 2020), p. 106897, 2021.
Huang H., Liu G., Deng L., Song T., and Qin F. P., Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation, Sci. Rep., 14 (1), p. 23589, 2024.
Ortiz S., Mallaiyan S. M. A., and Cha A., Time-of-flight camera characterization with functional modeling for synthetic scene generation, Opt. Express, 29(23), p. 37661, 2021.
Brunken H. and Gühmann C., Road Surface Reconstruction by Stereo Vision, PFG - J. Photogramm. Remote Sens. Geoinf. Sci., 88(6), pp. 433–448, 2020.
Huang S. Li, X., Liu Z., and Cheng K. T., Joint stereo 3D object detection and implicit surface reconstruction, Sci. Rep., 14(1), pp. 1–19, 2024.
Ojal N., Caviness A., Blum A., Au B., Jaycox A. W., and Giera B., Optimizing exposure times of structured light metrology systems using a digital twin, Meas. J. Int. Meas. Confed., 224 (November 2023), p. 113816, 2024.
Wang M., Sun Q., Gao C., Ren Z., and Dai W., A three-dimensional vision measurement method based on double-line combined structured light, Sci. Rep., 13(1), pp. 1–18, 2023.
Wang L., Lu D., Qiu R., and Tao J., 3D reconstruction from structured-light profilometry with dual-path hybrid network, EURASIP J. Adv. Signal Process., 2022 1 (2022).
Salvi J., Fernandez S., Pribanic T., and Llado X., A state of the art in structured light patterns for surface profilometry, Pattern Recognit., 43(8), pp. 2666–2680, 2010.
Lin S., Zhu H., and Guo H., Harmonics elimination in phase-shifting fringe projection profilometry by use of a non-filtering algorithm in frequency domain, Opt. Express, 31(16), p. 25490, 2023.
Huang T., Fu X., Li X., Zhang C., Duan F., Jiang J., and Tan H., Phase-to-depth calibration in fringe projection profilometry based on blockwise surface fitting, Opt. Laser Technol., 122 (July 2019), p. 105844, 2020.
Li J. and Li B., TPDNet: Texture-Guided Phase-to-DEPTH Networks to Repair Shadow-Induced Errors for Fringe Projection Profilometry, Photonics, 10(3), 2023.
Bai Y., Zhang Z., and Fu S., Recent Progress of Full-Field Three-Dimensional Shape Measurement Based on Phase Information,” Nanomanufacturing Metrol. 7(1) 2024.
Alkhatib M. N., Shmelev Y. D., Tyshova O. A., Sinilshchikov I. V., and Bobkov A. V., 3D measurement using fringe projection profilometry, Comput. Opt., 47(6), pp. 913–919, 2023.
Meng F., Wang F., Liu J., Chen M., and Wang Y., Phase shifting profilometry based on Hilbert transform: An efficient phase unwrapping algorithm,” J. Appl. Phys., 131(19), 2022.
Omidi P., Najiminaini M., Diop M., and Carson J. J. L., Single-shot 4-step phase-shifting multispectral fringe projection profilometry, Opt. Express, 29(18), p. 27975, 2021.
Lv S. and Kemao Q., Modeling the Measurement Precision of Fringe Projection Profilometry, Light Sci. Appl., 12(1), 2023.
Yang T., Zhang G., Li H., Zhang Z., and Zhou X., Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry, Opt. Lasers Eng., 123(February) pp. 37–44, 2019.
X. Song and L. Wang, “Dual-stage hybrid network for single-shot fringe projection profilometry based on a phase-height model,” Opt. Express, vol. 32, no. 1, p. 891, 2024, doi: 10.1364/oe.505544.
Boisvert J., Drouin M. A., Dicaire L. G., Picard M., and Godin G., Motion compensation for phase-shift structured-light systems based on a total-variation framework, Proc. - 2017 Int. Conf. 3D Vision, 3DV 2017, 1, pp. 658–666, 2018.
Li B., Liu Z., and Zhang S., Motion-induced error reduction by combining Fourier transform profilometry with phase-shifting profilometry, Opt. Express, 24(20), p. 23289, 2016.
Feng S., Zuo C., Tao T., Hu Y., Zhang M., Chen Q., and Gu G., Robust dynamic 3-D measurements with motion-compensated phase-shifting profilometry, Opt. Lasers Eng., 103(December 2017), pp. 127–138, 2018.
Hu P., Yang S., Deng H., and Zhang G., Motion-Induced Phase-Shifting Profilometry for Dynamic Objects Using Fourier Fringe Analysis and Speckle Correlation-Assisted Phase Matching, SSRN Electron. J., 33(8), pp. 18251–18263, 2022.
He K., Sui C., Huang T., Dai R., Lyu C., and Liu Y.H, 3D Surface reconstruction of transparent objects using laser scanning with LTFtF method, Opt. Lasers Eng., 148(May 2021), p. 106774, 2022.
Tang J. W., Liebner T. J., Craven B. A., and Settles G. S., A schlieren optical study of the human cough with and without wearing masks for aerosol infection control, J. R. Soc. Interface, 6(SUPPL. 6), pp. 727–736, 2009.
Hendriksen L. A., Sciacchitano A., and Scarano F., Object registration techniques for 3D particle tracking, Meas. Sci. Technol., 35(12), 2024.
Forgács L. and Antal A., Comparison of Structured Light Projection-based Surface Reconstruction Methods, Period. Polytech. Mech. Eng., 69(2), pp. 151–163, 2025.
Rosenberg O. I. and Abookasis D., Hybrid method combining orthogonal projection Fourier transform profilometry and laser speckle imaging for 3D visualization of flow profile, J. Mod. Opt., 67(13), pp. 1197–1209, 2020.
Hu E., Application of 3D Projection Profilometry in the High Speed Impaction Surface Deformation Measurement Research, J. Signal Inf. Process., 11(04), pp. 103–115, 2020.
Xu J. and Tian J., Accelerating fringe projection profilometry to 100k fps at high-resolution using deep learning, Light Sci. Appl., 14(1), 2025.
Wang B., Chen W., Qian J., Feng S., Chen Q., and Zuo C., Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning , Light Sci. Appl., 14 (70) 2025.
Machineni R. C., Spoorthi G. E., Vengala K. S., Gorthi S., and Gorthi R. K. S. S, End-to-end deep learning-based fringe projection framework for 3D profiling of objects, Comput. Vis. Image Underst., 199, p. 103023, 2020.
Takeda M. and Mutoh K., Fourier transform profilometry for the automatic measurement of 3-D object shapes, Appl. Opt., 22(24), p. 3977, 1983.
Hu Y., Chen Q., Feng S., and Zuo C., Microscopic fringe projection profilometry: A review, Opt. Lasers Eng., 135 (no. January), 2020.
Liu Y., Fu Y., Zhuan Y., Zhong K., and Guan B., High dynamic range real-time 3D measurement based on Fourier transform profilometry, Opt. Laser Technol., 138(no. January), p. 106833, 2021.
Zhang H., Zhang Q., Li Y., and Liu Y., High speed 3D shape measurement with temporal Fourier transform profilometry,” Appl. Sci., 9(19), 2019.
van Drongelen W., Signal Processing for Neuroscientists: Introduction to the Analysis of Physiological Signals, 2nd ed. Elsevier Ltd. pp. 103-118, 2018.
Li B., An Y., and Zhang S., Single-shot absolute 3D shape measurement with Fourier transform profilometry, Appl. Opt., 55(19), p. 5219, 2016.
Lafiosca P., Fan I. S., and Avdelidis N. P., Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm, Sensors, 22(2), 2022.
Liu Y., Zhang Q., Zhang H., Wu Z., and Chen W., Improve Temporal Fourier Transform Profilometry for Complex Dynamic Three-Dimensional Shape Measurement, Sensor (Basel) 20(7), 2020.
You Q., Weng H., Zhao J., Li Y., Wang W., Lu S., and Peng K., Fourier Transform Profilometry Based on Improved Goldstein Branch-Cut Algorithm,” Guangxue Xuebao/Acta Opt. Sin., 43(5), pp. 1–10, 2023.
Chen B., Li H., Yue J., and Shi P., Fourier-transform-based surface measurement and reconstruction of human face using the projection of monochromatic structured light, Sensors, 21(7), 2021.
Chen Q., Han M., Wang Y., and Chen W., An Improved Circular Fringe Fourier Transform Profilometry, Sensors, 22(16), 2022.
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