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A Theory of Shape by
Space Carving
Kiriakos N. Kutulakos
Department of Computer Science and Department of Dermatology, Uni...
Problem Statement
• Assuming:
– Arbitrary (known) camera placement.
– Arbitrary scene shape.
– Arbitrary scene texture, co...
Context
• Traditional Approach to 3D Reconstruction
– Correspondence based
“Many scenes can account for the input images”
Shape by Space Carving
• Scene or volume based
• No correspondences needed!
Picture-Shape Constraint
Photo Hull
• Definition: Maximal volume, V,
reproducing all possible silhouettes of an
object, S, from viewing region R.
R...
Photo Hull in 3D
Shape by Space Carving
• Given a volume containing the object, iteratively
carves it out until it converges to the Photo H...
Shape by Space Carving
Analogy
Shape by Space Carving
True Scene Reconstruction
Least-Commitment Recovery
Carving out
How to decide if a certain voxel belongs to the Photo Hull?
C
o n s i s t e n c y
Consistency
Rendering colored voxels from input
viewpoints reproduces original images.
Point photo-consistent: A point (vo...
Consistency (cont.)
Shape Photo-consistent: A shape for which all surface
points are photo-consistent.
Photo-Hull: Union o...
Proposed Algorithm
1. Initialize the volume containing the shape;
2. Analyze each visible voxel for point consistency
(pla...
Plane Sweep Strategy
Sequence of plane sweeps along 6 principle directions.
Only cameras behind the sweep plane are used f...
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Space Carving
Discussion
• Least-commitment Reconstruction
– No a-priori constraints
– Flexible acquisition
– Insensitivit...
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Leandro

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Transcript of "Leandro"

  1. 1. A Theory of Shape by Space Carving Kiriakos N. Kutulakos Department of Computer Science and Department of Dermatology, University of Rochester, Rochester, NY Steven M. Seitz The Robotics Institute, Carnegie Mellon University, Pittsburgh Published at the Special Issue on Genomic Signal Processing, 2000.
  2. 2. Problem Statement • Assuming: – Arbitrary (known) camera placement. – Arbitrary scene shape. – Arbitrary scene texture, color and background. – Lambertian. • Input: N photographs of a scene • Output: 3D structure of the scene
  3. 3. Context • Traditional Approach to 3D Reconstruction – Correspondence based “Many scenes can account for the input images”
  4. 4. Shape by Space Carving • Scene or volume based • No correspondences needed!
  5. 5. Picture-Shape Constraint
  6. 6. Photo Hull • Definition: Maximal volume, V, reproducing all possible silhouettes of an object, S, from viewing region R. R S V
  7. 7. Photo Hull in 3D
  8. 8. Shape by Space Carving • Given a volume containing the object, iteratively carves it out until it converges to the Photo Hull.
  9. 9. Shape by Space Carving Analogy
  10. 10. Shape by Space Carving True Scene Reconstruction Least-Commitment Recovery
  11. 11. Carving out How to decide if a certain voxel belongs to the Photo Hull? C o n s i s t e n c y
  12. 12. Consistency Rendering colored voxels from input viewpoints reproduces original images. Point photo-consistent: A point (voxel) that can be assigned a color consistent with its projection in all visible cameras. shape
  13. 13. Consistency (cont.) Shape Photo-consistent: A shape for which all surface points are photo-consistent. Photo-Hull: Union of all photo-consistent subsets of initial volume. VPhoto-hull of V Photo-consistent subsets of V
  14. 14. Proposed Algorithm 1. Initialize the volume containing the shape; 2. Analyze each visible voxel for point consistency (plane sweep strategy); 3. Repeat step 2 until no voxel is found inconsistent; 4. Merge photo consistent subsets.
  15. 15. Plane Sweep Strategy Sequence of plane sweeps along 6 principle directions. Only cameras behind the sweep plane are used for consistency checking.
  16. 16. Experimental Results
  17. 17. Experimental Results
  18. 18. Experimental Results
  19. 19. Experimental Results
  20. 20. Space Carving Discussion • Least-commitment Reconstruction – No a-priori constraints – Flexible acquisition – Insensitivity to occlusion – Efficient – Scalable • Setup – Camera calibration – Sensitivity to specularities and transparencies
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