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3D Scene Reconstruction by Stereo Methods for Analysis and Visualization of Sports Scenes

3D Scene Reconstruction by Stereo Methods for Analysis and Visualization of Sports Scenes. Margrit Gelautz, Michael Bleyer, Danijela Markovic and Christoph Rhemann Email: gelautz@ims.tuwien.ac.at Institute for Software Technology and Interactive Systems Vienna University of Technology, Austria.

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3D Scene Reconstruction by Stereo Methods for Analysis and Visualization of Sports Scenes

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  1. 3D Scene Reconstruction by Stereo Methods for Analysis and Visualization of Sports Scenes Margrit Gelautz, Michael Bleyer, Danijela Markovic and Christoph Rhemann Email: gelautz@ims.tuwien.ac.at Institute for Software Technology and Interactive Systems Vienna University of Technology, Austria

  2. Dagstuhl, Sept 7-10, 2008 Stereo Analysis • Principles of stereo analysis - Scene is observed by two cameras. - The key task in stereo analysis is the automated identification of corresponding points between the two input images (stereo matching problem). - The result of stereo analysis is a depth map.

  3. Dagstuhl, Sept 7-10, 2008 Stereo Matching Algorithm • Bleyer, M. and M. Gelautz, A Layered Stereo Matching Algorithm Using Image Segmentation and Global Visibility Constraints, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 59, no. 3, pp. 128-150, 2005. (Helava Award – best paper of the year). Left image Computed depth map Ground truth Right image Computed layers Error map

  4. Dagstuhl, Sept 7-10, 2008 Stereo Applications • Reconstruction of 3D shape of human body and posture • Example: Breakdance • Robotics and autonomous navigation • Novel view generation • Augmented and mixed reality (e.g. merging real with synthetic video content) • 3D (autostereoscopic) displays • …

  5. Dagstuhl, Sept 7-10, 2008 Soccer Scene – Stereo Analysis • Original images taken by stereo camera Left image Right image

  6. Dagstuhl, Sept 7-10, 2008 Soccer Scene – Novel View Synthesis • The stereo-derived depth map is computed and used to generate novel virtual views of the scene. Depth map Synthesized view

  7. Dagstuhl, Sept 7-10, 2008 Video Object Segmentation (1) • Automated extraction of video objects based on motion analysis and clustering of motion vectors. MPEG test sequence “Mobile & Calendar”

  8. Dagstuhl, Sept 7-10, 2008 Video Object Segmentation (2) • Automated segmentation of video object „Ball“. Overlay of segment borders. Extracted video object.

  9. Dagstuhl, Sept 7-10, 2008 Video Object Segmentation (3) • Video Compositing

  10. Dagstuhl, Sept 7-10, 2008 Stereo-based Visualization (1) • Stereo pair, depth and contour extraction Left image Right image Stereo depth map Significant contours

  11. Dagstuhl, Sept 7-10, 2008 Stereo-based Visualization (2) • Frame sequence

  12. Dagstuhl, Sept 7-10, 2008 Motion Tracking Tracking points Tracking path Curve fitting Multiple motion lines

  13. Dagstuhl, Sept 7-10, 2008 Comic-like Motion Style (1)

  14. Dagstuhl, Sept 7-10, 2008 Comic-like Motion Style (2)

  15. Dagstuhl, Sept 7-10, 2008 Comic-like Motion Style (3)

  16. References • Bleyer, M. and M. Gelautz, Graph-cut Based Stereo Matching Using Image Segmentation with Symmetrical Treatment of Occlusions, Signal Processing: Image Communication(Special Issue on Three-dimensional Video and Television), vol. 22, issue 2, pp. 127-143, 2007. • Markovic, D. and M. Gelautz, Comics-like Motion Depiction from Stereo, Proceedings of WSCG 2006, Plzen, Czech Republic, pp. 155-160, 2006. • Bleyer, M., C. Rhemann, and M. Gelautz, Segmentation-based Motion with Occlusions using Graph-cut Optimization, Proceedings of 28th Annual Symposium of the German Association for Pattern Recognition (DAGM 2006), Lecture Notes in Computer Science (LNCS) 4174, pp. 465–474, 2006.

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