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Improvements on Automated Registration

This project presentation discusses the motivations, problems, and implementations of automated registration for large-scale scene rendering. It explores techniques to improve correctness, user interaction, and results of the registration process. The future implementations focus on improving speed and global performance through clustering, estimation, and optimization.

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Improvements on Automated Registration

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  1. Improvements onAutomated Registration CSc83020 Project Presentation Cecilia Chao Chen

  2. References • [1] Automated Feature-based Range Registration of Urban Scenes of Large Scale, Ioannis Stamos and Marius Leordeanu • [2] Geometry and Texture Recovery of Scenes of Large Scale: Integration of Range and Intensity Sensing, Ioannis Stamos, Department of Computer Science, Columbia University 2

  3. Motivations • Three phases of 3D rendering large scale scenes: • Segmentation, Registration, Texture Mapping • Registration – an automated procedure in [1] • Pair-wise match two lines • Compute R, T and evaluate them • Keep the best R and T • Refine best R, T 3

  4. Motivations • Problems of automated registration • Mismatching wrong rotation + wrong translation + 4 overlapping area

  5. Motivations • Other problems • Slow for images with many parallel lines • lines in same direction => many possible R, T => long time to check • Error accumulation • I1-I2, I2-I3, I3-I4, I4-I5 pair-wise image registration • Err1, err2, err3, err4 from each registration above • I5-I1 == err? 5

  6. Is this correct? No Yes Display other best match results Hand-pick 3 point pairs to register No Is any correct? Yes Implementations • Improving the correctness • User interaction Automatic registration and display result 6

  7. Results • Hard to auto-register poorly overlapping images 7 Different viewpoints  different details  no matching lines in overlapping area

  8. Results • Hand-picking results in good registrations wrong rotation + correct registration 8

  9. Results • More hand-picking results wrong translation + correct registration 9

  10. Future Implementations • Improving speed • Cluster lines and find major directions • Estimate R • Compute T similarly to the original method • Expected to be much faster: O(m+n) vs. O(mn) • Improving global performance • Combine information after each registration • Global optimization by minimizing error function • Build user interface 10

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