Other Reconstruction Techniques

Transcription

1 Other Reconstruction Techniques Ruigang Yang CS 684 CS 684 Spring

2 Taxonomy of Range Sensing From Brain Curless, SIGGRAPH 00 Lecture notes CS 684 Spring

3 Taxonomy of Range Scanning (cont.) CS 684 Spring

4 Shape-from-X Different Shape cues Motion Focus/defocus Shading Specular Highlights Texture distortions Shadows CS 684 Spring

5 Structure-from-Motion (SfM) Similar to stereo, but the object or camera moves (rigid motion typically) Estimate both object shape and its relative motion Typical procedures Track features (line, corners, or texture patches) Estimate camera motion and object shape by solving a big linear system Refine the result using nonlinear optimization CS 684 Spring

6 Shape from Focus Obtain a set of images with different focus setting Image-processing to measure the quality of the focus for each pixel Best focus pixel depth Depth Input From Typical Use: microscopic images, i.e., extremely narrow DOF CS 684 Spring

7 Depth from Defocus The amount of blurriness depth CS 684 Spring

8 Depth from Defocus Problem: ambiguity CS 684 Spring

9 Depth from Defocus Solution: get one more image CS 684 Spring

10 Shape from Focus/defocus CS 684 Spring

11 Shape-from-Shading (SfS) Color variation surface normal depth For constant or smooth varying albedo surfaces In contrast to shape-from-focus/defocus CS 684 Spring

12 Example Left: Image of Agrippa (NE illumination) Right: 3D shape recovery (Tianzi Jiang, 1999). CS 684 Spring

13 from: CS 684 Spring

14 Shape from Shading (SfS) Problem: Given Reflectance map R(p,q) of viewed surface and knowledge of albedo ρ and direction n s of illuminant, reconstruct surface slopes (p,q) and surface Z=Z(X,Y). SFS: Direct Interpretation [Klette] Simplified Approach: Row Integration [Veridian] Propagation Methods [Horn 86, strip expansion] Global Minimization Approaches [Trucco pp.229, various others] CS 684 Spring

15 Shape from Textures Infer shape from the images from distortions in textures CS 684 Spring

16 Taxonomy of Range Scanning (cont.) CS 684 Spring

17 Scanning Methodology CS 684 Spring

18 Optical v.s. Laser Scanner Optical non-contact, safe, inexpensive, fast can acquire only visible surfaces, are sensitive to surface properties such as transparency, shininess, reflectance properties, etc. Laser compact, low power, easy to isolate single wavelength, no chromatic aberration Eye safety concerns, laser speckle adds noise (narrowing the aperture increases the noise). CS 684 Spring

19 Time of Flight Scanners Emit a pulse at t 1 Record the at t 2 Range (r): r = c(t 2 -t 1 )/2 CS 684 Spring

20 Triangulation Advantage: A strip at a time CS 684 Spring

21 Examples A laser light stripe Detected Light 3D Points Reconstructed Model Model with texture mapping CS 684 Spring

22 The Digital Michelangelo Project (Stanford 1999) CG Rendering CS 684 Spring

23 Active Stereo Project patterns to reduce ambiguity in stereo matching CS 684 Spring

24 Structured Light Use more patterns to eliminate ambiguity Binary Code A = 111 B = 110 C = 100 D = 011 CS 684 Spring

25 Active Depth from Defocus Nayar, S.K., Watanabe, M., and Noguchi, M. "Real-time focus range sensor", Fifth International Conference on Computer Vision (1995), pp CS 684 Spring

26 Moire Extract shape from interference patterns Illuminate a surface through a periodic grating. Capture image as seen at an angle through another grating. interference pattern, phase encodes shape Low pass filter the image to extract the phase signal. CS 684 Spring

27 Neat Idea beyond Lambertian Surfaces Insert a reference object of the same materials! A. Hertzmann and S. Seitz, CVPR 2003 CS 684 Spring

28 Another Example Result Laser Scan CS 684 Spring

29 More Materials Excellent search system for online course notes and presentations: CV Publications: Notes/bibliography/contents.html CS 684 Spring