1 / 20

Texture Compression for Large Real Environments

Texture Compression for Large Real Environments. Yizhou Yu Computer Science Division University of California at Berkeley. Varying Scene Configurations. Segment geometry into individual objects Related work

Download Presentation

Texture Compression for Large Real Environments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Texture Compression for Large Real Environments Yizhou Yu Computer Science Division University of California at Berkeley

  2. Varying Scene Configurations • 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

  3. 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 Framework photograph 3D mesh synthetic image range scan

  4. Overview Range Images Point Cloud Point Groups Meshes Registration Segmentation Reconstruction Pose Estimation Radiance Images Texture Maps Objects

  5. Image Segmentation as Graph Partitioning Build a weighted graph G=(V,E) from image V: image pixels E: connections between pairs of nearby pixels 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]

  6. Segmentation Results

  7. Image

  8. Aligning Photographs with Laser Scans • Pose estimation using calibration targets • 3 rotation and 3 translation parameters • Combinatorial search • 4 correspondences each image 3D Targets

  9. Camera Pose Results • Accuracy: consistently within 2 pixels • Correctness: correct pose for 58 out of 62 images

  10. Reconstructed Mesh with Camera Poses and Calibration Targets The “crust” algorithm, nearest-neighbors & quadric error metric

  11. Models of Individual Objects

  12. 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

  13. 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

  14. A Texture Map Packed with Triangular Texture Patches

  15. Texture-Mapping and Object Manipulation Original Configuration Novel Configuration

  16. 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.

  17. Results on Edge Detection

  18. Texture 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 3D Triangles Texture Map

  19. Synthetic Images with Compressed and Uncompressed Texture Maps Compressed 5 texture maps Uncompressed 20 texture maps 20 texture maps 5 texture maps

  20. Video

More Related