1 / 22

Evaluation of features detectors and descriptors based on 3D objects

Evaluation of features detectors and descriptors based on 3D objects. P. Moreels - P. Perona California Institute of Technology. Features – what for ?. Large baseline stereo. Object recognition. [Dorko & Schmid’05]. [Tuytelaars & Van Gool ’00]. Stitching. [Lowe ’04]. [Brown & Lowe ’03].

graham
Download Presentation

Evaluation of features detectors and descriptors based on 3D objects

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. Evaluation of features detectors and descriptors based on 3D objects P. Moreels - P. Perona California Institute of Technology

  2. Features – what for ? Large baseline stereo Object recognition [Dorko & Schmid’05] [Tuytelaars & Van Gool ’00] Stitching [Lowe ’04] [Brown & Lowe ’03]

  3. Moving the viewpoint

  4. Features stability Features stability is not perfect… 240 keypoints extracted 232 keypoints extracted

  5. First stage – feature detector Harris [Harris’88] difference of gaussians [Crowley’84] Affine invariant Harris [Mikolajczyk’02] Kadir & Brady [Kadir’02]

  6. Second stage – feature descriptor SIFT Steerable filters [Lowe ’04] [Freeman’91 ] Shape context Differential invariants [Schmid’97 ] [Belongie’02 ]

  7. Evaluations – Mikolajczyk ’03-’05 • Large viewpoint change • Computation of ground truth positions via a homography

  8. Evaluations – Mikolajczyk ’03-’05 • SIFT-based descriptors rule ! • All affine-invariant detectors are good, they should all be used together. [CVPR’03] [PAMI’04] [submitted]

  9. 2D vs. 3D Ranking of detectors/descriptors combinations are modified when switching from 2D to 3D objects

  10. Dataset – 100 3D objects

  11. Viewpoints 45° apart

  12. Viewpoints 45° apart

  13. Viewpoints 45° apart

  14. Viewpoints 45° apart

  15. Ground truth - Epipolar constraints

  16. Testing setup Unrelated images used to load the database of features.

  17. Distance ratio • Correct matches are highly distinctive  lower ratio • Incorrect correspondences are ‘random correspondences’  low distinctiveness and ratio close to 1 [Lowe’04]

  18. Are we accepting wrong matches ? Pietro said maybe don’t need this slide – I think it is important to justify our 3-cameras setup • Manual user classification into correct and incorrect triplets • Comparison with a simpler system: 2 views, only one epipolar constraint.

  19. Detectors / descriptors tested Detectors Descriptors • Harris • Hessian • Harris-affine • Hessian-affine • Difference-of-gaussians • MSER • Kadir-Brady • SIFT • steerable filters • differential invariants • shape context • PCA-SIFT

  20. Results – viewpoint change No ‘background’ images Mahalanobis distance

  21. Results – lighting / scale changes Change in light – result averaged over 3 lighting conditions. Change in scale - 7.0mm to 14.6mm

  22. Conclusions • Automated ground truth for 3D objects/scenes • Ranking changes from 2D to 3D • Stability is much lower for 3D • Detectors – affine-rectified detectors are indeed best • Descriptors – SIFT and shape context performed best. • Application: use ground truth in order to learn probability densities: ‘how does a correct match look like ?’

More Related