Texture-Mapping Real Scenes from Photographs

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SIGGRAPH 2000 Course on Image-Based Surface Details. Texture-Mapping Real Scenes from Photographs. Yizhou Yu Computer Science Division University of California at Berkeley. Basic Steps. Acquire Photographs Recover Geometry Align Photographs with Geometry Map Photographs onto Geometry.

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Presentation Transcript

SIGGRAPH 2000 Course on

Image-Based Surface Details

Texture-Mapping Real Scenes from Photographs

Yizhou Yu

Computer Science Division

University of California at Berkeley

Basic Steps
• Acquire Photographs
• Recover Geometry
• Align Photographs with Geometry
• Map Photographs onto Geometry
Camera Pose Estimation
• Input
• Known geometry recovered from photographs or laser range scanners
• A set of photographs taken with a camera
• Output
• For each photograph, 3 rotation and 3 translation parameters of the camera with respect to the geometry
• Requirement
• 4 correspondences between each photograph and the geometry
Recover Camera Pose with Known Correspondences
• Least-squares solution
• Needs good initial estimation from human interaction

Image

Camera

Recover Rotation Parameters only from Known Correspondences
• Constraints
• Least-squares solution

Image

Camera

Obtaining Correspondences
• Feature Detection in 3D geometry and 2D images
• Human interaction
• Interactively pick corresponding points in photographs and 3D geometry
• Automatic Search
• Combinatorial search
Automatic Search for Correspondences
• Pose estimation using calibration targets
• Combinatorial search for the best match
• 4 correspondences each image

3D Targets

Camera Pose Results
• Accuracy: consistently within 2 pixels

Texture-mapping a single image

Texture Mapping
• Conventional texture-mapping with texture coordinates
• Projective texture-mapping
Texture Map Synthesis I
• Conventional Texture-Mapping with Texture Coordinates
• Create a triangular texture patch for each triangle
• The texture patch is a weighted average of the image patches from multiple photographs
• Pixels that are close to image boundaries or viewed from a grazing angle obtain smaller weights

Photograph

3D Triangle

Texture Map

Texture Map Synthesis II
• Allocate space for texture patches from texture maps
• Generalization of memory allocation to 2D
• Quantize edge length to a power of 2
• Sort texture patches into decreasing order and use First-Fit strategy to allocate space

First-Fit

Texture-Mapping and Object Manipulation

Original Configuration

Novel Configuration

Texture Map Compression I
• The size of each texture patch is determined by the amount of color variations on its corresponding triangles in photographs.
• An edge detector (the derivative of the Gaussian) is used as a metric for variations.
Texture Map Compression II
• Reuse texture patches
• Map the same patch to multiple 3D triangles with similar color variations
• K-means clustering to generate texture patch representatives
• Larger penalty along triange edges to reduce Mach Band effect

3D Triangles

Texture Map

Compressed

5 texture maps

Uncompressed

20 texture maps

20 texture maps

5 texture maps

Projective Texture-Mapping
• Can directly use the original photographs in texture-mapping
• Visibility processing is more complicated
• Projective texture-mapping has been implemented in hardware, therefore, real-time rendering becomes possible
• View-dependent effects can be added by effectively using hardware accumulation buffer

Camera

Image

Geometry

Texture gets projected onto occluded and

backfacing polygons

Visibility Algorithms
• Image-space algorithms
• Ray casting
• Object-space algorithms
• Weiler-Atherton
A Hybrid Visibility Algorithm
• Occlusion testing in image-space using Z-buffer hardware
• Render polygons with their identifiers as colors
• Retrieve occluding polygons’ ids from color buffer
• Object-space shallow clipping to generate fewer polygons
Visibility Processing Results

The tower

The rest of the campus