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0 0 0 0 0 0 0 0 Kalyan Sunkavalli, Todd Zickler, and Hanspeter Pfister Harvard University Visibility Subspaces: Uncalibrated Photometric Stereo with Shadows 1 2 3 4 5 N A N B N C N D L 1 L 2 L 3 00 L 1 0 L 3 L 4 0 L 1 00 L 4 L 5 L 1 L 2 00 L 5 1 2 3 4 5 Scene B A C D Visibility regions Image matrix = Normals Lights Image s 2 4 1 Shadows and Scene Appearance Lambertian Scene A B C D Dimensionality of scene appearance with (cast) shadows The set of all images of a Lambertian scene illuminated by any combination of n directional light sources lies in a linear space with dimension at most 3(2 n ). Scene points with same visibility Rank-3 subspaces of image matrix (Visibility Subspaces) Subspace normals Estimated subspaces True subspaces Transformed normals Results Uncalibrated Photometric Stereo using Visibility Subspaces Input images (4 of 12) True subspaces Estimated subspaces Normals Depth Input images (4 of 8) True subspaces Estimated subspaces Normals Depth Estimating Visibility Subspaces Key Idea: Find subspaces by constructing lighting bases from randomly sampled points, and checking how many other points project onto these bases with low error (RANSAC). 1. Sample three points in scene and estimate lighting basis: 2. Compute normals at all other points using this lighting basis: 3. Compute error of estimated lights and normals at every point: 4. Mark points with error as inliers. Compute lighting basis from all inliers. 5. Repeat 1 through 4. Mark largest inlier-set found as subspace with lighting . 6. Repeat 1 through 5 until all visibility subspaces have been recovered. Images Subspaces to surface normals Key Idea: Estimate visibilities from subspaces, and use them to recover normals. Subspace clustering recovers normals up to a linear 3X3 ambiguity in each subspace: From , compute visibility: Using visibility, recover surface normals up to a single global linear 3X3 ambiguity by solving an over-constrained system of linear equations for global lighting and subspace ambiguities : For a Lambertian scene illuminated by lights , intensities at points in a subspace with visibility are given by: 3 5

Visibility Subspaces: Uncalibrated Photometric Stereo with Shadows

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Visibility Subspaces: Uncalibrated Photometric Stereo with Shadows. Kalyan Sunkavalli, Todd Zickler, and Hanspeter Pfister Harvard University. Shadows and Scene Appearance. Lambertian Scene. Scene points with same visibility. Rank-3 subspaces - PowerPoint PPT Presentation

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Page 1: Visibility Subspaces: Uncalibrated Photometric Stereo with Shadows

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Kalyan Sunkavalli, Todd Zickler, and Hanspeter Pfister Harvard UniversityVisibility Subspaces: Uncalibrated Photometric Stereo with Shadows

1 2 3 4 5NA

NB

NC

ND

L1 L2 L3 0 0L1 0 L3 L4 0L1 0 0 L4 L5

L1 L2 0 0 L5

1

2

3

4

5

Scene

B

A

C

D

Visibility regions Image matrix

=

Normals LightsImages

2

4

1

Shadows and Scene Appearance Lambertian Scene

ABCD

Dimensionality of scene appearance with (cast) shadowsThe set of all images of a Lambertian scene illuminated by any combination of n directional light sources lies in a linear space with dimension at most 3(2n).

Scene points with same visibility

Rank-3 subspaces of image matrix

(Visibility Subspaces)

Subspace normals

Estimated subspaces

True subspaces

Transformed normals

ResultsUncalibrated Photometric Stereo using Visibility Subspaces

Input images (4 of 12)

True subspaces

Estimated subspaces

Normals Depth

Input images (4 of 8)

True subspaces

Estimated subspaces

Normals Depth

Estimating Visibility SubspacesKey Idea: Find subspaces by constructing lighting bases from randomly sampled points, and checking how many other points project onto these bases with low error (RANSAC).

1. Sample three points in scene and estimate lighting basis:

2. Compute normals at all other points using this lighting basis:

3. Compute error of estimated lights and normals at every point:

4. Mark points with error as inliers. Compute lighting basis from all inliers.

5. Repeat 1 through 4. Mark largest inlier-set found as subspace with lighting .

6. Repeat 1 through 5 until all visibility subspaces have been recovered.

Images

Subspaces to surface normalsKey Idea: Estimate visibilities from subspaces, and use them to recover normals.• Subspace clustering recovers normals up to a linear 3X3 ambiguity in each subspace:

• From , compute visibility:• Using visibility, recover surface normals up to a single global linear 3X3 ambiguity by

solving an over-constrained system of linear equations for global lighting and subspace ambiguities :

For a Lambertian scene illuminated by lights , intensities at points in a subspace with visibility are given by:

3

5