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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
  • 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: Artifacts Caused by Hardware




Texture gets projected onto occluded and

backfacing polygons

visibility algorithms
Visibility Algorithms
  • Image-space algorithms
    • Shadow buffer
    • Ray casting
  • Object-space algorithms
    • Weiler-Atherton
a hybrid visibility algorithm
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
Visibility Processing Results

The tower

The rest of the campus