1 / 19

SuperMatching : Feature Matching using Supersymmetric Geometric Constraints

SuperMatching is a revolutionary matching algorithm designed for graphics and vision tasks, particularly in the realm of 3D shape analysis. It utilizes feature tuples, such as triangles, formulated within a supersymmetric higher-order affinity tensor to achieve precise pairwise matching of complex 3D rigid shapes. This technique is demonstrated through the matching of Rooster scans and articulated shapes, showcasing its capability against traditional methods like 4PCS. SuperMatching offers notable accuracy and competitive time costs, utilizing real 3D data sourced from Kinect.

arty
Download Presentation

SuperMatching : Feature Matching using Supersymmetric Geometric Constraints

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. SuperMatching: Feature Matching using Supersymmetric Geometric Constraints Submission ID: 0208

  2. Overview • SuperMatching is: • A fundamental matching algorithm in GRAPHics and VISION tasks

  3. Overview • SuperMatching is: • A fundamental matching algorithm in GRAPHics and VISION tasks Pairwise matching using uniformly sampled points on the 3D shapes

  4. Overview • SuperMatching is: • Using feature tuples (triangles or higher-order tuples) • Formulated as a supersymmetrichigher-order affinity tensor

  5. Overview • SuperMatching is: • Using feature tuples (triangles or higher-order tuples) • Formulated as a supersymmetrichigher-order affinity tensor Third-order diagram (edge length invariance in 3D triangles)

  6. 3D rigid shapes scans • Pairwise matching of Rooster scans I II II III Initial poses Matching result

  7. 3D rigid shapes scans • Pairwise matching of Rooster scans I II II III Initial poses Matching result

  8. 3D rigid shapes scans • Comparison with 4PCS [Aiger et al. 2008] SuperMatching [Aiger et al. 2008] Rooster II-III pairwise registration

  9. 3D rigid shapes scans • Comparison with 4PCS [Aiger et al. 2008] SuperMatching [Aiger et al. 2008] Rooster II-III pairwise registration

  10. 3D real depth scans • Colored Scene captured by Kinect Source shape Target shape Pairwise Matching Final alignment

  11. 3D real depth scans • Colored Scene captured by Kinect

  12. 3D articulated shapes • ArticulatedRobot between frame 9 and 10 distortion SuperMatching [Chang and Zwicker 2009]

  13. 3D articulated shapes • ArticulatedRobot between frame 9 and 10 SuperMatching [Chang and Zwicker 2009]

  14. Deformable surfaces Spectral method [Cour et al. 2006] Hypergraph matching [Zass and Shashua 2008] A third-order tensor [Duchenne et al. 2009] SuperMatching cloth: F80-F90 cushion: F144-F156

  15. Deformable surfaces • Accuracy and Time-costs

  16. Deformable surfaces • Accuracy and Time-costs More accurate with competitive time

  17. Deformable surfaces • Accuracy and Time-costs More accurate with competitive time

  18. Thanks Real 3D data captured by Kinect

  19. Thanks JOBS Real 3D data captured by Kinect

More Related