1. Acknowledgement: some content and figures by Brian Curless
3D Scanning
Acknowledgement: some content and figures by Brian Curless

2. Data Types Volumetric Data Surface Data Voxel grids Occupancy Density
Point clouds
Range images (range maps)

3. Related Fields Computer Vision Metrology Passive range sensing
Rarely construct complete, accurate models
Application: recognition
Metrology
Main goal: absolute accuracy
High precision, provable errors more important than scanning speed, complete coverage
Applications: industrial inspection, quality control, as-built models

4. Related Fields Computer Graphics Often want complete model
Low noise, geometrically consistent model more important than absolute accuracy
Application: animated CG characters

5. Terminology
Range acquisition, shape acquisition, rangefinding, range scanning, 3D scanning
Alignment, registration
Surface reconstruction, 3D scan merging, scan integration, surface extraction
3D model acquisition

6. Range Acquisition Taxonomy
Mechanical (CMM, jointed arm)
Inertial (gyroscope, accelerometer)
Contact
Ultrasonic trackers
Magnetic trackers
Industrial CT
Range acquisition
Transmissive
Ultrasound
MRI
Radar
Non-optical
Sonar
Reflective
Optical

7. Range Acquisition Taxonomy
Shape from X:
stereo
motion
shading
texture
focus
defocus
Passive
Optical methods
Active variants of passive methods
Stereo w. projected texture
Active depth from defocus
Photometric stereo
Active
Time of flight
Triangulation

8. Optical Range Scanning Methods
Advantages:
Non-contact
Safe
Usually inexpensive
Usually fast
Disadvantages:
Sensitive to transparency
Confused by specularity and interreflection
Texture (helps some methods, hurts others)

9. Stereo
Find feature in one image, search along epipole in other image for correspondence

10. Stereo Advantages: Disadvantages:
Passive
Cheap hardware (2 cameras)
Easy to accommodate motion
Intuitive analogue to human vision
Disadvantages:
Only acquire good data at “features”
Sparse, relatively noisy data (correspondence is hard)
Bad around silhouettes
Confused by non-diffuse surfaces
Variant: multibaseline stereo to reduce ambiguity

11. Shape from Motion “Limiting case” of multibaseline stereo
Track a feature in a video sequence
For n frames and f features, have 2nf knowns, 6n+3f unknowns

12. Shape from Motion Advantages: Disadvantages:
Feature tracking easier than correspondence in far-away views
Mathematically more stable (large baseline)
Disadvantages:
Does not accommodate object motion
Still problems in areas of low texture, in non-diffuse regions, and around silhouettes

13. Shape from Shading
Given: image of surface with known, constant reflectance under known point light
Estimate normals, integrate to find surface
Problem: ambiguity

14. Shape from Shading Advantages: Disadvantages: Not really practical
Single image
No correspondences
Analogue in human vision
Disadvantages:
Mathematically unstable
Can’t have texture
Not really practical
But see photometric stereo

15. Shape from Texture
Mathematically similar to shape from shading, but uses stretch and shrink of a (regular) texture

16. Shape from Texture Analogue to human vision
Same disadvantages as shape from shading

17. Shape from Focus and Defocus
Shape from focus: at which focus setting is a given image region sharpest?
Shape from defocus: how out-of-focus is each image region?
Passive versions rarely used
Active depth from defocus can be made practical

18. Active Optical Methods
Advantages:
Usually can get dense data
Usually much more robust and accurate than passive techniques
Disadvantages:
Introduces light into scene (distracting, etc.)
Not motivated by human vision

19. Active Variants of Passive Techniques
Regular stereo with projected texture
Provides features for correspondence
Active depth from defocus
Known pattern helps to estimate defocus
Photometric stereo
Shape from shading with multiple known lights

20. Pulsed Time of Flight
Basic idea: send out pulse of light (usually laser), time how long it takes to return

21. Pulsed Time of Flight Advantages: Disadvantages:
Large working volume (up to 100 m.)
Disadvantages:
Not-so-great accuracy (at best ~5 mm.)
Requires getting timing to ~30 picoseconds
Does not scale with working volume
Often used for scanning buildings, rooms, archeological sites, etc.

22. AM Modulation Time of Flight
Modulate a laser at frequencym , it returns with a phase shift 
Note the ambiguity in the measured phase!  Range ambiguity of 1/2mn

23. AM Modulation Time of Flight
Accuracy / working volume tradeoff (e.g., noise ~ 1/500 working volume)
In practice, often used for room-sized environments (cheaper, more accurate than pulsed time of flight)

24. Triangulation

25. Triangulation: Moving the Camera and Illumination
Moving independently leads to problems with focus, resolution
Most scanners mount camera and light source rigidly, move them as a unit

26. Triangulation: Moving the Camera and Illumination

27. Triangulation: Moving the Camera and Illumination

28. Triangulation: Extending to 3D
Possibility #1: add another mirror (flying spot)
Possibility #2: project a stripe, not a dot
Laser
Object
Camera

29. Triangulation Scanner Issues
Accuracy proportional to working volume (typical is ~1000:1)
Scales down to small working vol. (e.g. 5 cm. working volume, 50 m. accuracy)
Does not scale up (baseline too large…)
Two-line-of-sight problem (shadowing from either camera or laser)
Triangulation angle: non-uniform resolution if too small, shadowing if too big (useful range: 15-30)

30. Triangulation Scanner Issues
Material properties (dark, specular)
Subsurface scattering
Laser speckle
Edge curl
Texture embossing

31. Multi-Stripe Triangulation
To go faster, project multiple stripes
But which stripe is which?
Answer #1: assume surface continuity

32. Multi-Stripe Triangulation
To go faster, project multiple stripes
But which stripe is which?
Answer #2: colored stripes (or dots)

33. Multi-Stripe Triangulation
To go faster, project multiple stripes
But which stripe is which?
Answer #3: time-coded stripes

34. Time-Coded Light Patterns
Assign each stripe a unique illumination code over time [Posdamer 82]
Time
Space

35. Gray-Code Patterns
To minimize effects of quantization error: each point may be a boundary only once
Time
Space