1. Matching and Recognition in 3D

2. Moving from 2D to 3D Some things harderSome things harder – Rigid transform has 6 degrees of freedom vs. 3 – No natural parameterization (e.g. running FFT or convolution is trickier) Some things easierSome things easier – No occlusion (but sometimes missing data instead) – Segmenting objects often simpler

3. Matching / Recognition in 3D Methods from 2DMethods from 2D – Feature detectors – Histograms – PCA (eigenshapes) – Graph matching, interpretation trees

4. Matching / Recognition in 3D Other methods (may also apply to 2D)Other methods (may also apply to 2D) – Identifying objects in scene: spin images – Finding a single object in a database: shape distributions – Aligning pieces of the same object: iterative closest points (ICP)

5. 3D Identification Using Spin Images Spin images: Johnson and HebertSpin images: Johnson and Hebert “Signature” that captures local shape“Signature” that captures local shape Similar shapes  similar spin imagesSimilar shapes  similar spin images

6. 3D Identification Using Spin Images General approach:General approach: – Create database of many objects, many spin images for each object – For each point in unknown scene, compute spin image – Find matches in database – Compare object in database to scene

7. Computing Spin Images Start with a point on a 3D modelStart with a point on a 3D model Find (averaged) surface normal at that pointFind (averaged) surface normal at that point Define coordinate system centered at this point, oriented according to surface normal and two (arbitrary) tangentsDefine coordinate system centered at this point, oriented according to surface normal and two (arbitrary) tangents Express other points (within some distance) in terms of the new coordinatesExpress other points (within some distance) in terms of the new coordinates

8. Computing Spin Images Compute histogram of locations of other points, in new coordinate system, ignoring rotation around normal:Compute histogram of locations of other points, in new coordinate system, ignoring rotation around normal:

9. Computing Spin Images

10. Spin Image Parameters Size of neighborhoodSize of neighborhood – Determines whether local or global shape is captured – Big neighborhood: more discriminatory power – Small neighborhood: resistance to clutter Size of bins in histogram:Size of bins in histogram: – Big bins: less sensitive to noise – Small bins: captures more detail, less storage

11. Spin Image Results Range Image Model in Database

12. Spin Image Results Detected Models

13. Shape Distributions Osada, Funkhouser, Chazelle, and DobkinOsada, Funkhouser, Chazelle, and Dobkin Compact representation for entire 3D objectCompact representation for entire 3D object Invariant under translation, rotation, scaleInvariant under translation, rotation, scale Application: search engine for 3D shapesApplication: search engine for 3D shapes

14. Computing Shape Distributions Pick n random pairs of points on the objectPick n random pairs of points on the object Compute histogram of distancesCompute histogram of distances Normalize for scaleNormalize for scale 3D Model Shape Distribution Random sampling

15. Comparing Shape Distributions Similarity Measure 3DModelShapeDistribution

16. Shape Distributions for Simple Shapes

17. Robustness Results 7 Mugs 7 Missiles

18. Classification Results

20. 3D Alignment Alignment of partially-overlapping (pieces of) 3D objectsAlignment of partially-overlapping (pieces of) 3D objects Application: building a complete 3D model given output of stereo, 3D scanner, etc.Application: building a complete 3D model given output of stereo, 3D scanner, etc. One possibility: spin imagesOne possibility: spin images Another possibility: correspondences from user inputAnother possibility: correspondences from user input

21. Iterative Closest Points (ICP) Besl & McKay, 1992Besl & McKay, 1992 Start with rough guess for alignment from:Start with rough guess for alignment from: – Tracking position of scanner – Spin images – User input Iteratively refine transformIteratively refine transform Output: high-quality alignmentOutput: high-quality alignment

22. Aligning Scans Start with manual initial alignmentStart with manual initial alignment [Pulli]

23. Aligning Scans Improve alignment using ICP algorithmImprove alignment using ICP algorithm [Pulli]

24. ICP Assume closest points correspond to each other, compute the best transform…Assume closest points correspond to each other, compute the best transform…

25. ICP … and iterate to find alignment… and iterate to find alignment Converges to some local minimumConverges to some local minimum Correct if starting position “close enough“Correct if starting position “close enough“