1 image-based visual hulls paper by wojciech matusik, chris buehler, ramesh raskar, steven j....

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Image-Based Visual Hulls

Paper by Wojciech Matusik, Chris Buehler, Ramesh Raskar,

Steven J. Gortler and Leonard McMillan[http://graphics.lcs.mit.edu/~wojciech/vh/]

Vortrag von Simon DellenbachGDV Fachseminar 2001

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Overview (1)

• Motivation

• Basics

– Viewpoint Model

– Visual Hull

– Epipolar Geometry

• Creating Image-Based Visual Hulls

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Overview (2)

• Rendering IBVH

• System Implementation

• Summary & Results

• Future Work

• Personal Opinion

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Motivation (1)

• Traditional computer graphics, rendering..– static synthetic scenes (CG Images)– dynamic synthetic scenes (CG Animations)– static acquired scenes (Image-Based Rendering)

• Acquire and render dynamic scenes in real-time:– appropriate representation– rendering system

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Motivation (2)

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Viewpoint Model - Basics (1)

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Visual Hull - Basics (2)

• Geometric shape obtained using silhouettes of object seen from number of views:– extruded silhouette = cone-like volume limiting

the extent of object– intersection of volumes results in a visual hull– more views better approximation of object– limitation: concavities can’t be captured

(e.g. an open box looks like a solid cube)

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Visual Hull - Basics (3)

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Epipolar Geometry - Basics (4)

• The tree points [COP1,COP2,P] form an epipolar plane

• Intersection of this plane with image planes results in epipolar lines

• The line connecting the two centers of projection [COP1,COP2] intersects the image planes at the conjugate points e1 and e2 which are called epipoles

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Epipolar Geometry - Basics (5)

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Creating Image-Based Visual Hulls (1)

• Algorithm input:– set of k silhouettes (binary images) with

associated viewpoints– desired viewpoint (in this case, constructed

visual hull is viewpoint-dependent)

• Algorithm output:– sampled image of the visual hull, each pixel

containing a list of occupied intervals of space

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Creating Image-Based Visual Hulls (2)

• The Basic Algorithm:– cast ray into space for each pixel in the

desired view of the visual hull– intersect ray with the k silhouette cones

k lists of intervals; intersect together single list of intersections of the viewing ray with the visual hull

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Creating Image-Based Visual Hulls (3)

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Creating Image-Based Visual Hulls (4)

• Trick: due to Epipolar Geometry interval calculation can be done in image space of reference images:– 3D: intersecting silhouette cone with viewing

ray– 2D: intersecting projected viewing ray with

silhouette

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Creating Image-Based Visual Hulls (5)

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Rendering IBVH (1)

• Reference images are used as textures• For each pixel:

– rank reference-image texture from “best” to “worst” according to angle, take reference with lowest

– avoid texturing surface points with an image whose line-of-sight is blocked by some other point of the visual hull

– consider visibility during shading based on visual hull (not actual geometry)

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Rendering IBVH (2)

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Rendering IBVH (3)

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System Implementation (1)

• Four calibrated and triggered digital cameras

• One desktop PC per camera for capturing and pre-processing video frames (image segmentation)

• Silhouette and texture information sent to central server for IBVH processing

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System Implementation (2)

• Server runs IBVH intersection and shading algorithms

• IBVH objects can be combined with OpenGL background

• System runs in ‘real time’ with heavy optimization (like caching strategies for silhouette intersection)

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System Implementation (3)

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Summary & Results

• Use visual hull as object shape approximation• Using silhouette information from reference

views to generate view dependent visual hull• Reference images are used as ‘textures’

• Results:Videoclips

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Future Work

• Find Techniques for blending between textures to produce smoother transitions

• Scale up system by using larger number of cameras

• Split workload on multiple servers, as algorithm parallelizes fairly much

• Speed up viewing ray silhouette intersections (most expensive part of the computation)

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Personal Opinion (1)

• Pros:– simple technique / low-cost hardware– image-based representation partially

compensates simplification problems– epipolar geometry reduces 3D-intersection

problems to 2D-intersections

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Personal Opinion (2)

• Cons:– texture flipping during viewpoint transitions

produces ugly results– shadows are considered as part of the object– preprocessing is really expensive

(85 ms for image foreground segmentation)

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The End

“If there are no questions,there won’t be any answers.”

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