3 D Scanning Acknowledgement some content and figures

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

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

Related Fields • Computer Vision – 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

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

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

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

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

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)

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

Stereo • Advantages: – 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

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

Shape from Motion • Advantages: – 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 nondiffuse regions, and around silhouettes

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

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

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

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

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

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

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

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

Pulsed Time of Flight • Advantages: – 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.

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

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)

Triangulation

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

Triangulation: Moving the Camera and Illumination

Triangulation: Moving the Camera and Illumination

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

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 )

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

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

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

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

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

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