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A Fast Local Descriptor for Dense Matching

A Fast Local Descriptor for Dense Matching. Engin Tola , Vincent Lepetit , Pascal Fua Computer Vision Laboratory, EPFL Reporter ： Jheng-You Lin. Outline. Introduction DAISY Computation Results C onclusion. Introduction.

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A Fast Local Descriptor for Dense Matching

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  1. A Fast Local Descriptor for Dense Matching EnginTola, Vincent Lepetit, Pascal Fua Computer Vision Laboratory, EPFL Reporter：Jheng-You Lin

  2. Outline • Introduction • DAISY Computation • Results • Conclusion

  3. Introduction • Wide-base line matching propose : SIFT、GLOH、SURF… (histogram based descriptor) • Good performance and robustness to image transformations. • High computational cost and sensitivity to occlusions. • Purpose • Design a descriptor that is as robust as SIFT or GLOH but can be computed much more effectively and handle occlusions.

  4. Introduction (cont.) • Novelty • introduces DAISY local image descriptor

  5. Introduction (cont.) • Novelty • introduces DAISY local image descriptor * S. Winder and M. Brown. Learning Local Image Descriptors in CVPR’07 • Improved performance: • + Precise localization • + Rotational Robustness

  6. Introduction (cont.) • Novelty • introduces DAISY local image descriptor Replacing weighted sums by convolutions

  7. DAISY Computation

  8. DAISY Computation First compute gradient magnitude layers in different orientations

  9. DAISY Computation Then, apply convolution with a Gaussian kernel to pre-compute the histograms for every point

  10. DAISY Computation

  11. DAISY Computation

  12. DAISY Computation

  13. DAISY Computation The computation mostly involves 1D convolutions, which is fast.

  14. DAISY Computation Rotating the descriptor only involves reordering the histograms. …

  15. DAISY Computation Rotating the descriptor only involves reordering the histograms. …

  16. DAISY Computation Computation Time Comparison(in seconds)

  17. DAISY Computation The full DAISY descriptor D(u, v) : Normalize to unit norm The descriptor of the same point that is close to an occlusion would be very different.

  18. Results Laser scan DAISY SIFT SURF NCC Pixel Difference

  19. Results baseline increase block • Error threshold : • Top : 10% • Middle : 5% • Bottom : 1% NCC DAISY SIFT SURF SURF Pixel Difference

  20. Results 768x510 (2048x1360 origin) Using low-resolution of the Brussels images[24] [24] Combined Depth and Outlier Estimation in Multi-View Stereo, CVPR’06

  21. Results 768x512 (3072x2048 origin) Using low-resolution of the Rathaus images[25] The holes are caused by the fact that a lot of the texture is not visible. [25] Dense Matching of Multiple Wide-Baseline Views, ICCV’03

  22. Results Input images Virtual view Synthesized

  23. Results Virtual view Synthesized DAISY NCC

  24. Conclusion • Efficient descriptor and produces good reconstructions. • Can handle low quality imagery

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