Super-Resolution Texturing for Online Virtual Globes

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Presentation transcript:

Super-Resolution Texturing for Online Virtual Globes Internet Vision Workshop (CVPR 2008) Super-Resolution Texturing for Online Virtual Globes Diego Rother, Lance Williams and Guillermo Sapiro University of Minnesota and Google, Inc.

Online Virtual Globes

Online Virtual Globes Requests Tiles Server Client User Problems: ClipMap pyramid. Problems: Huge storage space required (one of the largest organized collections of imagery on the Internet). Expensive acquisition and high transmission bandwidth. Interpolation beyond available resolution: unnatural.

Earth surface Stereotypical. Rapidly changing. Identity important, details not.

Proposed Solution Proposition: Requirements: Synthesize, on the client, details for the lower pyramid levels. Using super-resolution techniques. Harnessing labels and textures samples from users (wiki model). Requirements: Fast. Seamless transition between layers.

Results

User input 1: Labels Original frame User provided labels Class2 (path) (grass) Using interactive segmentation as in: Bai, X. and Sapiro, G., "A geodesic framework for fast interactive image and video segmentation and matting." ICCV, 2007.

User input 2: Keyframed Texture User provides the texture pyramid: Keyscale1 e.g., in meters/pixel Keyframe1 Keyscale2 Keyframe2 Texture pyramid.

Synthesis of a New Layer Input: from Server Input: from Users Texture pyramid. Output: New Layer ClipMap pyramid Labels

System Overview Inputs Outputs New Labels Labels (from server) Pyramid of Training Textures (from users) Inputs (from server) Last Layer Labels Outputs New Layer New Labels Interpolation Color Matching Texture Transfer Undo Color Matching Selection of the Training Image

Texture transfer: 1st pass Training Texture (from the texture pyramid) Color matched image, without high frequencies Y Channel (luminance) I and Q Channels (chrominance) Mean and Gradient Only Mean Similarity between contexts considers Source Locations Produces 1-Pixel Patches: Contiguous areas copied verbatim from the training texture. Small contexts → Fast Wei, L. and Levoy, M., "Fast Texture Synthesis using Tree-structured Vector Quantization." SIGGRAPH, 2000. Efros, A. A. and Leung, T. K., "Texture Synthesis by Non-parametric Sampling." ICCV, 1999.

Texture transfer: 2nd, 3rd and 4th passes Color Channel Pass 2nd 3rd and 4th Y Channel (luminance) I and Q Channels (chrominance) Similarity between contexts considers Training Texture Produces Bigger Patches Few candidates → Fast Ashikhmin, M., "Synthesizing Natural Textures." ACM Symposium on Interactive 3D Graphics. 2001.

Texture transfer from the same texture 1st synthetic frame 2nd synthetic frame ClipMap Pyramid Texture Pyramid Patch interior (lilac and violet) directly copied. Patch boundaries (pink) synthesized in 4 passes as before. Doubles the patch size.

No texture transferred Results: Maracanã ClipMap pyramid (result) Texture pyramid (user input) No texture transferred

Results: Maracana ClipMap pyramid (result) Source Locations (result) No texture transferred No texture transferred No texture transferred No texture transferred

Results: Field ClipMap pyramid (result) Texture pyramid (user input) No texture transferred Source Locations (result) No texture transferred

Results: Beach ClipMap pyramid (result)

Conclusions Proposed solution: Reduces storage, bandwidth, and acquisition costs. Improves appearance and information content. Is fast (low dimensional contexts).