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3D Surface Reconstruction from 2D Images (Survey)

2006. 11. 3 (Fri) Young Ki Baik, Computer Vision Lab. . 3D Surface Reconstruction from 2D Images (Survey). 3D Surface Reconstruction from 2D Images. References A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms Steven M. Seitz, Richard Szeliski et. al. (CVPR 2006)

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3D Surface Reconstruction from 2D Images (Survey)

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  1. 2006. 11. 3 (Fri) Young Ki Baik, Computer Vision Lab. 3D Surface Reconstruction from 2D Images (Survey)

  2. 3D Surface Reconstruction from 2D Images • References • A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms • Steven M. Seitz, Richard Szeliski et. al. (CVPR 2006) • A Survey of Methods for Volumetric Scene Reconstruction • Greg Slabaugh et. al. (VG 2001) : Volume Graphics

  3. 3D Surface Reconstruction from 2D Images • Contents • Introduction • Camera calibration • Shape from 2D images, techniques • Conclusion

  4. 3D Surface Reconstruction from 2D Images • Introduction • Volumetric data representations • Gaining importance since their introduction in the early 70’s →3D medical imaging [Greenleaf 70]

  5. 3D Surface Reconstruction from 2D Images • Introduction • Volumetric data representation • The exponential growth of computational storage and processing →practical alternatives to surface based geometrical representation for many applications in computer graphics and scientific visualization

  6. 3D Surface Reconstruction from 2D Images • Application • Reverse engineering • Augmented reality • Human computer interaction • Animation, Game • Etc.

  7. 3D Surface Reconstruction from 2D Images • Methods for volumetric reconstruction • By hand • 3D tool (3DMAX, MAYA, …) • Limitation • Too much time, tedious

  8. 3D Surface Reconstruction from 2D Images • Methods for volumetric reconstruction • Laser scanner (Range data) • Advantage • High quality and accuracy • Limitation • Too much money • Specific configuration

  9. 3D Surface Reconstruction from 2D Images • Methods for volumetric reconstruction • CCD camera (Images) • Advantage • Cheap price • Usefulness

  10. 3D Surface Reconstruction from 2D Images • Condition of 3D reconstruction 3D point 3D object mapping Image plane Camera Camera Camera system for obtaining images

  11. 3D Surface Reconstruction from 2D Images • Condition of 3D reconstruction • Point correspondence • Camera parameter and motion 3D point 3D object Camera Camera 3D reconstruction system to make 3D object

  12. 3D Surface Reconstruction from 2D Images • Camera Calibration • Camera parameters: • Extrinsic: Translation T, Rotation R. • Intrinsic: Focal Length f, image center (ox ,oy), effective pixel size (sx ,sy), radial distortion k. • Recover parameters from 3D points and their projections. Object View9 View1 View6 Camera Motion

  13. 3D Surface Reconstruction from 2D Images • Camera Calibration • Simple overall flow • Camera is set fixed location. • Obtaining camera parameters using projected 2D image and world 3D data with Known plane or 3D rig. • We can acquire 3D volumetric representation by applying various reconstruction algorithm.

  14. 3D Surface Reconstruction from 2D Images • Camera Calibration • Calibration with pattern: • Tsai’s method [Tsai87] • Zhang’s method [Zhang00] • Self-calibration [Maybank92] • Bundle adjustment [Triggs], evenly distribute errors.

  15. 3D Surface Reconstruction from 2D Images • Shape from 2D Images • Shape from silhouette • Shape from structured light • Shape from Illumination • Shape from shading • Photometric stereo • Shape from Color(or intensity) • Voxel coloring • Stereo vision • Fusion method

  16. 3D Surface Reconstruction from 2D Images • Shape from Silhouette(SFS) • Early works of vision • Effective method • Sculpturing a statue

  17. 3D Surface Reconstruction from 2D Images • Shape from Silhouette(SFS) - Back-project each silhouette along the ray - Obtain 3D volumetric data from intersecting back-projected volume - Calibrated cameras and object - Set initial 3D volumetric region including object O1 O2 O3

  18. 3D Surface Reconstruction from 2D Images • Shape from Silhouette(SFS) • Advantages: • Simple to implement and fairly robust • Fast execution • complete closed surface → commonly used as the effective initial boundary • Limitations: • only produced line hull • can’t detect non-convex region • sensitive to segmentation result → specific color is used as the background

  19. 3D Surface Reconstruction from 2D Images • Shape from Structured Light • Shape from Structured Light • Rays coming out of light source hit the object surface and captured by image sensor (usually a camera) in a different angle. [Levoy00, Allen03] • Problem • - Optically uncooperative materials • - Scanning in the presence of occlusion • - Filling holes in dense polygon models

  20. 3D Surface Reconstruction from 2D Images • Shape from Illumination • Shape from Shading • Assume distance point light source, orthographic projection, local shading and Lambertian surface • Given image intensity, determine depth by solving reflectance map in the fields of Radiometry. • Lambertian • A surface point is equally bright from all directions. • Limitation • - Do not provide qualified results

  21. 3D Surface Reconstruction from 2D Images • Shape from Illumination • Photometric stereo • Advanced version of shape from shading • Method to determine surface shape using multiple images taken by varying illumination direction, while fixed camera position • Advantage • - Provide good results relative to shape from shading • Limitation • - Have to know the location of light sources

  22. Sees blue Sees blue 3D Surface Reconstruction from 2D Images • Shape from Color • Voxel Coloring • Images can be constraints on 3D scene: • a valid 3D scene model projected must produce synthetic images same as the corresponding real input images. • SFS+color consistency • Opaque or not Sees red Sees green

  23. 3D Surface Reconstruction from 2D Images • Shape from Color • Voxel Coloring (overall flow) - Place the object to the fixed location. - Set the camera on the fixed location. - Set up voxel region covering object.

  24. 3D Surface Reconstruction from 2D Images • Shape from Color • Voxel Coloring (overall flow) - Iterate this algorithm about all voxel in the region. - Select a voxel and project onto the each image. - Judge opaqueness by thresholding variance of colors.

  25. 3D Surface Reconstruction from 2D Images • Voxel Coloring • Advantages: • simple to implement and fairly robust • Limitations: • performance depends on voxel and image resolution. →reconstruct object in small area →high computational cost • occlusion and illumination problem

  26. 3D Surface Reconstruction from 2D Images • Voxel coloring • More advanced algorithm • Space carving • Generalized voxel coloring • Multi-hypothesis voxel coloring

  27. Scene object point Epipolar plane Optical axes Epipolar lines Image plane Left Camera Left Camera 3D Surface Reconstruction from 2D Images • Shape from color • Stereo vision • Features • popular method • pixel based method • mimics the behavior of human vision • apply feature matching criterion at all pixels simultaneously • search only over epipolar lines (fewer candidate positions)

  28. 3D Surface Reconstruction from 2D Images • Shape from color • Stereo vision • Matching cost • Squared Intensity Differences (SD,SSD). • Absolutely Intensity Differences (AD,MSE). • Normalized Cross-correlation, normalized SSD.

  29. 3D Surface Reconstruction from 2D Images • Stereo vision • Advantages • gives detailed surface estimates • covering wide area object • Building, topography • Fast execution • multi-view aggregation improves accuracy

  30. 3D Surface Reconstruction from 2D Images • Stereo vision • Limitation • narrow baseline give rise to noisy estimates • fails in ~ • textureless and occlusion areas • sparse, in complete surface • sensitive to non-Lambertian effects. • Other effective methods • http://cat.middlebury.edu/stereo/

  31. 3D Surface Reconstruction from 2D Images • Fusion methods • Silhouette and Stereo Fusion for 3D Object Modeling - Carlos Hernandez Esteban and Francis Schmitt (CVIU 2004) # SFS +multi +stereo correlation voting +Gradient vector flow +Snake • High-Fidelity Image-Based Modeling - Yasutaka Furukawa, Jean Ponce (CVR-TR-2006) # SFS + wide baseline matching + propagation + Energy minimization • Multi-View Stereo Revisited - Michael Goesele et. al. (CVPR2006) # SFS +Stereo matching + volumetric method (range data + Level Set) • MultiView Geometry for Texture mapping 2D Images Onto 3D Range Data - Lingyoun Liu et. Al. (CVPR2006) # SFS +Stereo matching + volumetric method (range data)

  32. 3D Surface Reconstruction from 2D Images • Trend of 3D reconstruction method • ~1995 Simple algorithm • ~1999 VC and variants (treating occlusion), LevelSet, optimization • ~2001 Probabilistic formulation • ~2003 Non-Lambatian surface, specular surface, textureless regions • ~2006 PGM, Fusion method (SFS + Stereo + Level set + …)

  33. 3D Surface Reconstruction from 2D Images • Conclusion • Survey of methods for volumetric scene reconstruction from photographs. • States of the arts shows very good reconstruction results. • All algorithm do not solve problems yet. • occlusion ,illumination changes • non-Lambatian surface • Real data (no silhouette) • There is room for improvement

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