| 3. Feature
matching |
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| • | 3D object
recognition |
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| – | Assume affine
transform: clusters of size >=3 |
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| – | Looking for 3
matches out of 3000 that agree on |
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| same object and
pose: too many outliers for |
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| RANSAC or LMS |
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| – | Use Hough
Transform |
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| • | Each match votes
for a hypothesis for object ID/pose |
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| • | Voting for
multiple bins & large bin size allow for error due |
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| to similarity
approximation |
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