Texture Compression for Large Real Environments
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Transcript of Texture Compression for Large Real Environments
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Yizhou Yu
Texture Compression for Large Real Texture Compression for Large Real EnvironmentsEnvironments
Texture Compression for Large Real Texture Compression for Large Real EnvironmentsEnvironments
Yizhou Yu
Computer Science Division
University of California at Berkeley
Yizhou Yu
Computer Science Division
University of California at Berkeley
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Yizhou Yu
Varying Scene ConfigurationsVarying Scene ConfigurationsVarying Scene ConfigurationsVarying Scene Configurations
• Segment geometry into individual objects
• Related work– [ Hoffman & Jain 87 ], [ Besl & Jain 88 ], [ Newman
Flynn & Jain 93 ], [ Leonardis, Gupta & Bajcsy 95 ]
• Segment geometry into individual objects
• Related work– [ Hoffman & Jain 87 ], [ Besl & Jain 88 ], [ Newman
Flynn & Jain 93 ], [ Leonardis, Gupta & Bajcsy 95 ]
Original Configuration Novel Configuration
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Yizhou Yu
FrameworkFrameworkFrameworkFramework
• Input– Multiple range scans of a
scene
– Multiple photographs of the same scene
• Input– Multiple range scans of a
scene
– Multiple photographs of the same scene
• Output– Geometric meshes of each
object in the scene
– Registered texture maps for objects
• Output– Geometric meshes of each
object in the scene
– Registered texture maps for objects
photograph
range scan 3D meshsynthetic image
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Yizhou Yu
OverviewOverviewOverviewOverview
RangeImages
RadianceImages
PointCloud
PointGroups Meshes
TextureMaps Objects
Registration Segmentation Reconstruction
PoseEstimation
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Yizhou Yu
Image Segmentation as Graph PartitioningImage Segmentation as Graph PartitioningImage Segmentation as Graph PartitioningImage Segmentation as Graph PartitioningBuild a weighted graph G=(V,E) from image
V: image pixels
E: connections between pairs of nearby pixels
region
same the tobelong
j& iy that probabilit :ijW
Partition graph so that similarity within group is large and similarity between groups is small -- Normalized Cuts Approximate solution from a generalized eigenvalue problem [Shi&Malik 97]
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Yizhou Yu
Segmentation ResultsSegmentation ResultsSegmentation ResultsSegmentation Results
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Yizhou Yu
ImageImageImageImage
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Yizhou Yu
Aligning Photographs with Laser ScansAligning Photographs with Laser ScansAligning Photographs with Laser ScansAligning Photographs with Laser Scans
• Pose estimation using calibration targets– 3 rotation and 3 translation parameters
• Combinatorial search– 4 correspondences each image
• Pose estimation using calibration targets– 3 rotation and 3 translation parameters
• Combinatorial search– 4 correspondences each image
3D Targets
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Yizhou Yu
Camera Pose ResultsCamera Pose ResultsCamera Pose ResultsCamera Pose Results
• Accuracy: consistently within 2 pixels• Correctness: correct pose for 58 out of 62 images
• Accuracy: consistently within 2 pixels• Correctness: correct pose for 58 out of 62 images
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Yizhou Yu
Reconstructed Mesh with Camera Poses Reconstructed Mesh with Camera Poses and Calibration Targetsand Calibration TargetsReconstructed Mesh with Camera Poses Reconstructed Mesh with Camera Poses and Calibration Targetsand Calibration Targets
The “crust” algorithm, nearest-neighbors & quadric error metric
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Yizhou Yu
Models of Individual ObjectsModels of Individual ObjectsModels of Individual ObjectsModels of Individual Objects
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Yizhou Yu
Texture Map Synthesis ITexture Map Synthesis ITexture Map Synthesis ITexture 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
• 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
Texture Map
3D Triangle
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Yizhou Yu
Texture Map Synthesis IITexture Map Synthesis IITexture Map Synthesis IITexture 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
• 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
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Yizhou Yu
A Texture Map Packed with Triangular A Texture Map Packed with Triangular Texture PatchesTexture PatchesA Texture Map Packed with Triangular A Texture Map Packed with Triangular Texture PatchesTexture Patches
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Yizhou Yu
Texture-Mapping and Object ManipulationTexture-Mapping and Object ManipulationTexture-Mapping and Object ManipulationTexture-Mapping and Object Manipulation
Original Configuration
Novel Configuration
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Yizhou Yu
Texture Map Compression ITexture Map Compression ITexture Map Compression ITexture 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.
• 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.
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Yizhou Yu
Results on Edge DetectionResults on Edge DetectionResults on Edge DetectionResults on Edge Detection
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Yizhou Yu
Texture Map Compression IITexture Map Compression IITexture Map Compression IITexture Map Compression II
• Reuse texture patches– Map the same patch to
multiple 3D triangles with similar color variations
• K-mean clustering to generate texture patch representatives
• Larger penalty along triange edges to reduce Mach Band effect
• Binary search to find the number of clusters
• Reuse texture patches– Map the same patch to
multiple 3D triangles with similar color variations
• K-mean clustering to generate texture patch representatives
• Larger penalty along triange edges to reduce Mach Band effect
• Binary search to find the number of clustersTexture Map
3D Triangles
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Yizhou Yu
Synthetic Images with Compressed and Synthetic Images with Compressed and Uncompressed Texture MapsUncompressed Texture MapsSynthetic Images with Compressed and Synthetic Images with Compressed and Uncompressed Texture MapsUncompressed Texture Maps
Uncompressed20 texture maps
Compressed5 texture maps
20 texture maps 5 texture maps
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Yizhou Yu
VideoVideoVideoVideo