1 / 28

Yongho Shin

Efficient High-Resolution Stereo Matching using Local Plane Sweeps Sudipta N. Sinha, Daniel Scharstein , Richard Szeliski @ CVPR 2014. Yongho Shin. Problems. High-resolution images require long time for computing a disparity map Complexity for general local methods for 2x size images. x4.

arion
Download Presentation

Yongho Shin

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. EfficientHigh-Resolution Stereo Matching using Local Plane SweepsSudipta N. Sinha, Daniel Scharstein, Richard Szeliski @ CVPR 2014 Yongho Shin

  2. Problems • High-resolution images require long time for computing a disparity map • Complexity for general local methods for 2x size images x4

  3. Related works • Semi-global matching • Optimize following energy function • NP-hard problem!! • Approximate methods operate in adequate computing time, but still slow • Dynamic programming gives faster way, but erroneous result • Instead do dynamic programming along many directions • It cannot model second-order smoothness

  4. Related works • Efficient large-scale stereo matching • Stereo matching based on search space reduction • Computation GCPs • Delaunay triangulation on GCPs • Matching on triangles with restricted range

  5. Segment-Based Stereo Matching Using Belief Propagation Very related work

  6. Matching with a segmentation • Initial matching • Any matching method can be used • Initial matching • Extraction of • reliable pixels • Extraction of • model parameter • from each segment • Assignment of • optimal parameter • for each segment by BP Noisy result

  7. Matching with a segmentation • Extraction of reliable pixels • Simple cross checking method is used • Occlusion region can be detected • Initial matching • Extraction of • reliable pixels • Extraction of • model parameter • from each segment Left image Right image • Assignment of • optimal parameter • for each segment by BP Left result Right result

  8. Matching with a segmentation • Extraction of model parameter from each segment • At each segment, a model parameter is extracted using reliable pixels and robust statistical technique • Add the parameter to a parameter set • Initial matching • Extraction of • reliable pixels • Extraction of • model parameter • from each segment • Assignment of • optimal parameter • for each segment by BP Reliable pixels Segments

  9. Matching with a segmentation • Extraction of model parameter from each segment • At each segment, a model parameter is extracted using reliable pixels and robust statistical technique • Add the parameter to a parameter set • Initial matching • Extraction of • reliable pixels Parameter • Extraction of • model parameter • from each segment Parameter Set • Assignment of • optimal parameter • for each segment by BP

  10. Matching with a segmentation • Assignment of optimal parameter for each segment by BP • Assign an optimal parameter for each segment as total energy can be minimized • Initial matching • Extraction of • reliable pixels Parameter #29 Parameter #29 • Extraction of • model parameter • from each segment Parameter Set • Assignment of • optimal parameter • for each segment by BP

  11. Matching with a segmentation a b c a : Initial disparity map b : Interpolated result c : Reliable pixel map d : Result from a segmentation d

  12. Matching with a segmentation • What they did • Make plane parameter by segment and initial disparity map • Find optimal plane parameters for each segment of the image • Select optimal parameters by BP

  13. Proposed method

  14. Information for understanding • What they do • Make plane parameter by feature points • Find optimal plane parameters for each tiles of the image • Allowing objects having curved surface • Select optimal parameters by SGM

  15. Hypothesis generation Proposed method

  16. Hypothesis generation • Feature matching • By Harris corner keypoints and upright DAISY descriptors • Matching only points along near epipolar line • Due to stereo matching • But, they allow small vertical misalignments • First round • Initial set of matches are selected using the ratio test heuristic • Second round • For obtaining more matched features • Horizontal search range is reduced using local estimates

  17. Hypothesis generation • Vertical alignment • Correct for small vertical misalignments from errors in rectification • By fitting a global linear model using RANSAC with matched features

  18. Hypothesis generation • Disparity plane estimation • Cluster matched points and find plane parameters • Find k number of planes • Using variational approach used for mesh simplification • Graph based approach with priority queue

  19. Local plane sweeps Proposed method

  20. Local plane sweep • Plane for tiles having parallax • Because there are curved objects in the world • Hence, gives range of ±T pixels of parallax from plane • For each plane, investigate similarity among range 2T • Optimize by SGM

  21. Local plane sweep • Identifying in-range disparities • By disparity map, they give cost U AD NCC JUMP

  22. Proposal generation Proposed method

  23. Proposal generation • Initial proposals • Find the planes with associated points within each tile • Online proposals • Find frequent plane parameter for each tile • Propagate the parameter to neighbors

  24. Global optimization Proposed method

  25. Global optimization • We have • Plane parameters for each tile • Cost U • Energy function • Power SGM!!

  26. Experiments

  27. Quantitative results

  28. Qualitative results

More Related