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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

Image

Camera

recover rotation parameters only from known correspondences
Recover Rotation Parameters only from Known Correspondences
  • Constraints
  • Least-squares solution

Image

Camera

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

Photograph

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

First-Fit

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

Compressed

5 texture maps

Uncompressed

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

Camera

Image

Geometry

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