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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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Texture mapping real scenes from photographs

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
Basic Steps

  • Acquire Photographs

  • Recover Geometry

  • Align Photographs with Geometry

  • Map Photographs onto Geometry

Camera pose estimation
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
Recover Camera Pose with Known Correspondences

  • Least-squares solution

    • Needs good initial estimation from human interaction



Recover rotation parameters only from known correspondences
Recover Rotation Parameters only from Known Correspondences

  • Constraints

  • Least-squares solution



Obtaining correspondences
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
Automatic Search for Correspondences

  • Pose estimation using calibration targets

  • Combinatorial search for the best match

    • 4 correspondences each image

3D Targets

Camera pose results
Camera Pose Results

  • Accuracy: consistently within 2 pixels

Texture-mapping a single image

Texture mapping
Texture Mapping

  • Conventional texture-mapping with texture coordinates

  • Projective texture-mapping

Texture map synthesis i
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


3D Triangle

Texture Map

Texture map synthesis ii
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


Texture mapping and object manipulation
Texture-Mapping and Object Manipulation

Original Configuration

Novel Configuration

Texture map compression i
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
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

Synthetic images with compressed and uncompressed texture maps
Synthetic Images with Compressed and Uncompressed Texture Maps


5 texture maps


20 texture maps

20 texture maps

5 texture maps

Projective texture mapping
Projective Texture-Mapping Maps

  • 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

Motivation for visibility processing artifacts caused by hardware
Motivation for Visibility Processing: MapsArtifacts Caused by Hardware




Texture gets projected onto occluded and

backfacing polygons

Visibility algorithms
Visibility Algorithms Maps

  • Image-space algorithms

    • Shadow buffer

    • Ray casting

  • Object-space algorithms

    • Weiler-Atherton

A hybrid visibility algorithm
A Hybrid Visibility Algorithm Maps

  • 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
Visibility Processing Results Maps

The tower

The rest of the campus