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3D Multi-view Reconstruction

3D Multi-view Reconstruction. Young Min Kim Karen Zhu CS 223B March 17, 2008. Outline. Problem Data Set MRF Noise Reduction Multi-view Reconstruction Conclusion. Problem. Create broad-view high-resolution 3D view. 3D View. Normal Camera. Depth Camera. Data Set. MRF.

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3D Multi-view Reconstruction

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  1. 3D Multi-view Reconstruction Young Min Kim Karen Zhu CS 223B March 17, 2008

  2. Outline • Problem • Data Set • MRF • Noise Reduction • Multi-view Reconstruction • Conclusion

  3. Problem • Create broad-view high-resolution 3D view 3D View Normal Camera Depth Camera

  4. Data Set

  5. MRF • Single view super-resolution reconstruction • Objective function: E=Ed+Ec • Ed: Similarity between the up-sampled depth and the depth sensor measurement • Ec: Regions with similar color have similar depth • mrfDepthSmooth code from Stephen Gould [1] James Diebel, Sebastian Thrun, “An Application of Markov Random Fields to Range Sensing”, Proceedings of Conference on Neural Information Processing Systems (NIPS), MIT Press, Cambridge, MA, 2005.

  6. Original depth map MRF result MRF: Result

  7. Noise Reduction • Single-view Improvement • Median filtering • Occlusion boundary removal Original depth image Median filtered depth image

  8. Original MRF Median filtered Occlusion boundary removed

  9. Multi-view Reconstruction Problem: misalignment

  10. New Objective function using multi-view information: E=Ed+Ec+Em Em: similarity between depth in two different view Multi-view Reconstruction

  11. Multi-view Reconstruction

  12. Multi-view Reconstruction: Result

  13. Conclusion • Median filter is effective in removing sensor noise • Removing occlusion boundary reduce noise due to motion • By using information from multi-view, depth images are better aligned

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