Dense Stereo, Optical Flow and View Synthesis
Dense stereo and optical flow aim to estimate a dense correspondence field between a given pair of images taken either from different viewpoints or at different time instants.
- D. Min, J. Lu, and M. N. Do, “A Revisit to Cost Aggregation in Stereo Matching: How Far Can We Reduce Its Computational Redundancy?,” IEEE Int. Conf. on Computer Vision (ICCV), Nov. 2011. (Oral) [pdf]
- D. Min, J. Lu, and M. N. Do, “Joint Histogram Based Cost Aggregation for Stereo Matching,” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 35, no. 10, pp. 2539-2545, Oct. 2013. [pdf]
- J. Lu, H. Yang, D. Min, and M. N. Do, “PatchMatch Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation,” IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Jun. 2013. (Oral) [pdf][ppt]
- B. Ham, D. Min, C. Oh, M. N. Do, and K. Sohn, “Probability-Based Rendering for View Synthesis,” IEEE Trans. on Image Processing (TIP), vol. 23, no. 2, pp. 870-884, Feb. 2014. [pdf][web]
- D. Vu, B. Chidester, H. Yang, M. N. Do, and J. Lu, “Efficient Hybrid Tree-Based Stereo Matching with Applications to Post-Capture Image Refocusing,” IEEE Trans. Image Processing (TIP), vol. 23, no. 8, pp. 3428-3442, Aug. 2014. [pdf][demo]
- K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-Scale Cost Aggregation for Stereo Matching,” IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Jun. 2014. [pdf]
- J. Lu, Y. Li, H. Yang, D. Min, W. Eng, and M. N. Do, “PatchMatch Filter: Edge-Aware Filtering Meets Randomized Search for Correspondence Field Estimation,” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 39, no. 9, pp. 1866-1879, Sep. 2017. [preprint]
For more information, please visit our website for a half-day tutorial in ICME 2015.