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Indexing with Unknown Illumination and Pose Ira Kemelmacher Ronen Basri Weizmann Institute of Science

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The task – Shape Indexing Came l Hippo Dino 2. Recover pose+lighting 1. Recognize …

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Why is it hard? Unknown pose Unknown lighting Clutter Occlusion

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Assumptions Weak perspective projection 3D rigid transformation Lambertian model

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Previous… Identification using alignment Fischler and Bolles, Huttenlocher and Ullman “3D to 2D invariants do not exist” Burns etal., Moses etal., Clemens etal. Indexing faster than alignment Jacobs, Wolfson etal.

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Previous Indexing Methods Ignored intensity information Need many point or line features Restricted to polyhedral objects

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Our algorithm… Handles both pose and lighting Uses intensities to filter out incorrect matches Still relies on point features but only very few are needed General objects

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Indexing with pose - Affine model (Jacobs ‘96) p i = AP i + t 8 DOF -> 5 points (n 1,n 2,m) (α 3, β 3 ) (α 4, β 4 ) p0p0 p1p1 p2p2 p3p3 p4p4 P0P0 P1P1 P3P3 P4P4 P2P2

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(n 1,n 2,m) P0P0 P1P1 P3P3 P4P4 P2P2 Representation in two 2D tables Online – matching Offline – preprocessing (α 3, β 3 ) (α 4, β 4 ) p0p0 p1p1 p2p2 p3p3 p4p4 α -space α 4 = α 3 m + n 1 (α 3, α 4 ) β -space β 4 = β 3 m + n 2 (β 3, β 4 )

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Modifications – still two 2D spaces Online – matching Offline – preprocessing (α 3, β 3 ) (α 4, β 4 ) p0p0 p1p1 p2p2 p3p3 p4p4 α –dual-space (n 1,n 2,m) P0P0 P1P1 P3P3 P4P4 P2P2 r 1 = α 3 cos θ+ α 4 sinθ (θ,r 1 ) β –dual-space r 2 = β 3 cos θ+ β 4 sinθ (θ,r 2 )

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One 3D space Online – matching Offline – preprocessing (α 3, β 3 ) (α 4, β 4 ) p0p0 p1p1 p2p2 p3p3 p4p4 (n 1,n 2,m) P0P0 P1P1 P3P3 P4P4 P2P2 (θ,r 1,r 2 )

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False Matches True match

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How to eliminate the false matches Enforce rigidity using inverse Gramian Test - Weinshall, ‘93 Consistency with lighting NEXT

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Harmonic Images – Linear Basis for Lighting (Basri and Jacobs ‘01, Ramamoorthi and Hanrahan ‘01)

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Representation by harmonics I = b * H Harmonics of model point set Unknown light Intensitie s of image point set

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The consistency measure For corresponding image and model sets this is minimal I = b * H Should we apply it on feature points?

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“Smooth points” Smooth points Feature points

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Voting Sets of points that pass the lighting test vote for their respective model All models receive scores: Score = fraction of image sets for which the model appears min Once model is selected its corresponding subsets used to determine its pose and lighting

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Experiments Real 3d objects acquired using laser scanner Feature points collected automatically using Harris corner detector

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Results dino hippo camel shark pinokio bear elephant face

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Results dinohippocamel shark pinokio bear elephant face

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Results

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Results – Indoor scene dinohippocamel shark pinokio bear elephant face

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Results – Outdoor scene dino camel shark pinokio bear elephant face hippo

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Results – Night Scene dino camel shark pinokio bear elephant face hippo

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Results – Night Scene 2 dino camel shark pinokio bear elephant face hippo

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Filtering out matches

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How much does lighting help? Voting based on Affine model

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How much does lighting help? Affine + Rigidity test

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How much does lighting help? Affine + Rigidity + Lighting

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Conclusion Identify 3d objects in 2d scenes Unknown pose, light Clutter, occlusions General, real objects Fast, efficient Combination of intensity cues and geometry

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Thank you!

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