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Digitalization of Cultural Artifacts using Structured Lighting

This paper explores innovative methods for the digitalization of cultural artifacts through structured lighting techniques. The study highlights an automatic procedure for reconstructing high-detailed surfaces in a non-tactile, non-destructive manner, compared to traditional CAD approaches. It emphasizes key processes such as calibration, depth measuring, stereo correspondence, and dynamic thresholding to accurately capture the shape of cultural objects from 2D images. The research aims to enhance the preservation of heritage through advanced computer vision methods.

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Digitalization of Cultural Artifacts using Structured Lighting

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  1. Digitalization of Cultural Artifacts using Structured Lighting H.J. Chien 簡祥任 C.Y. Chen 陳佳妍 C.F. Chen 陳基發 Y.M. Su 蘇義明

  2. Outline • Introduction • Structured Lighting • Calibration • Depth measuring • Stereo Correspondence • Dynamic Thresholding • Sub-stripe Resolution • Reconstructed Result • Conclusion and Future Work Vision and Graphics Lab, Dept. CSIE, NUK

  3. Introduction • Heritage Preserve & Computer Vision • Digitalized shape or structure of cultural objects from 2D images • Reconstructed results are not relevant to modeling skill • comparing to CAD approaches • Automatic procedure • Fast acquisition • Highly detailed surface • Non-tactile, non-destructive scanning Vision and Graphics Lab, Dept. CSIE, NUK

  4. Introduction • Related work • Stanford Digital MichelangeloProject (1999) • Statue Maddalena • Bronze Minerva of Arezzo • Castles in Northern Italy • Old city of Xanthi in Greek • Great Buddhas in Japan • … Bronze Minerva of Arezzo C. Rocchini, et al, “A Low Cost 3D Scanner based on Structured Light” Proc. Eurographics 2001, vol. 20(3), pp. 299-308, 2001. Stanford Digital Michelangelo Project http://graphics.stanford.edu/projects/mich/ Vision and Graphics Lab, Dept. CSIE, NUK

  5. Structured Lighting • Stem from passive stereo vision • One of paired cameras is replaced by a projector • Actively solve correspondence problem • Able to obtain dense depthmap on featureless surface • Challenges • How to calibrate a projector • How to establish correspondence • How to extract light patterns • Heterogeneity of image resolutions • e.g. 1600x1200 (camera) vs. 800x600 (projector) Vision and Graphics Lab, Dept. CSIE, NUK

  6. Structured Lighting • Overall Strategy • Lens Calibration • Camera • Projector • Correspondence • Sequential scanning • Simple patterns • Coded patterns • Triangulation Vision and Graphics Lab, Dept. CSIE, NUK

  7. Calibration - Camera • Tsai’s method • One-frame calibration • Need object with known geometry as reference • Construct linear equations using perspective projection model • Solve linear system • Refine result by non-linear optimization (aka bundle adjustment) • e.g. Gradient descent, Levenberg-Marquardt Vision and Graphics Lab, Dept. CSIE, NUK

  8. Calibration - Projector • Treat projector as an inversed pinhole camera • Reconstruct projector view • from camera view and camera-projector correspondence • Apply Tsai’s method Projector View (partial) Camera View Vision and Graphics Lab, Dept. CSIE, NUK

  9. Depth Measuring • Triangulation technique • Find intersection of the back-projected ray through camera and the corresponding projection plane Vision and Graphics Lab, Dept. CSIE, NUK

  10. Depth Measuring • Visualized example Vision and Graphics Lab, Dept. CSIE, NUK

  11. Stereo Correspondence • Plane-to-plane mapping • State how two image planes are related • Key prerequisite to reconstruction techniques based on multiple view geometry Vision and Graphics Lab, Dept. CSIE, NUK

  12. Stereo Correspondence • Gray-coded light patterns • Time-multiplexed coding strategy • Use binary Gray codes to represent 1D coordinate of each unit stripe on projector’s image plane • N projections identify2n unit stripes Vision and Graphics Lab, Dept. CSIE, NUK

  13. Binarization • Intensity-ratio thresholding • Store per-pixel intensity profile • Compute intensity ratio • Binarize each pixel base onits ratio • Invariant lightingcondition? Vision and Graphics Lab, Dept. CSIE, NUK

  14. Observation Vision and Graphics Lab, Dept. CSIE, NUK

  15. Observation Decision Boundary Pattern # Vision and Graphics Lab, Dept. CSIE, NUK

  16. Dynamic Thresholding • Albedo-based thresholding fails • when the lighting condition of a pixel is away from the reference images • Need to update thresholds • Use an online updating mechanism • Probabilistic kernel-based dichotomizer weighting coefficient kernel function Vision and Graphics Lab, Dept. CSIE, NUK

  17. Per-Pixel Classification K = Gaussian Kernel (σ: 0.1) Vision and Graphics Lab, Dept. CSIE, NUK

  18. Fixed vs. Dynamic Thresholding • Result on a problematic pixel Pattern # Vision and Graphics Lab, Dept. CSIE, NUK

  19. Sub-stripe Resolution • Information lost in camera-projector correspondence • due to… • heterogeneous resolution, improper placement, wrongly decoded index, noises, etc. • Cause measurement errors • Estimate missing mappings • By interpolation • Assign new values to unsureelements • Reference reliable neighbors Vision and Graphics Lab, Dept. CSIE, NUK

  20. Sub-stripe Resolution • How to tell reliability of a pixel • Take certainty of classification into account • Defined as sum of square of P(Ri=1~k) for each pixel • Adjustment on correspondence • F: decoded correspondence • C: certainty of classification • D: function reflects spatial proximity • G: adjusted correspondence Vision and Graphics Lab, Dept. CSIE, NUK

  21. Sub-stripe Resolution • Result CpX9 : X-coordinate correspondence established usingnine Gray-coded light patterns Z9 : Depth computed using CpX9, saw-toothed effects are significant Zconv : Improved result using densified correspondence Zspline : Improved result using spline interpolation Vision and Graphics Lab, Dept. CSIE, NUK

  22. Reconstructed Result • Chosen artifact 布偶如來 • Has various surfaces in different material • Non-uniform reflectance • Specular and diffuse componentsare present • Hardware & Configuration Vision and Graphics Lab, Dept. CSIE, NUK

  23. Reconstructed Result • Original surface Vision and Graphics Lab, Dept. CSIE, NUK

  24. Reconstructed Result • Improved surface Vision and Graphics Lab, Dept. CSIE, NUK

  25. Reconstructed Result • Improved surface (textured) Vision and Graphics Lab, Dept. CSIE, NUK

  26. Conclusion & Future Work Vision and Graphics Lab, Dept. CSIE, NUK

  27. Showcase http://ndap.csie.nuk.edu.tw Vision and Graphics Lab, Dept. CSIE, NUK

  28. Vision and Graphics Lab, Dept. CSIE, NUK

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