1. Speaker Min-Koo Kang March 26, 2013 Depth Enhancement Technique by Sensor Fusion: MRF-based approach

2. 2 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Outline 1. Review of filter-based method - Summary and limitation 2. Related work - MRF-based depth up-sampling framework 3. Introduction of state-of-the-art method - High Quality Depth Map Upsampling for 3D-TOF Cameras / ICCV 2011 4. Future work - Remaining problems - Strategy

3. 3 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Depth upsampling  Definition  Conversion of depth map with low resolution into one with high resolution  Approach  Most state-of-the-art methods are based on sensor fusion technique; i.e., use image sensor and range sensor together Depth map up-sampling by using bi-cubic interpolation Depth map up-sampling by using image and range sensor

4. 4 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Joint bilateral upsampling (JBU)  Representative formulation:  N(P): targeting pixel P(i, j)’s neighborhood. f S (.): spatial weighting term, applied for pixel position P. f I (.): range weighting term, applied for pixel value I(q). f S (.), f I (.) are Gaussian functions with standard deviations, σ S and σ I, respectively. *Kopf et al., “Joint Bilateral Upsampling”, SIGGRAPH 2007 Upsampled depth map Rendered 3D view

5. 5 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Is JBU ideal enough?  Limitations of JBU:  It starts from the fundamental heuristic assumptions about the relationship between depth and intensity data  Sometimes depth has no corresponding edges in the 2-D image  Remaining problems:  Erroneous copying of 2-D texture into actually smooth geometries within the depth map  Unwanted artifact known as edge blurring High-resolution guidance image (red=non-visible depth discontinuities) Low-resolution depth map (red=zooming area) JBU enhanced depth map (zoomed)

6. 6 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Summary of JBU-based approach  Joint bilateral upsampling approach  Propagates properties from one to an other modality  Credibility map decides system performance  Defining blending function can be another critical factor  Many empirical parameters make the practical automated usage of such fusion filter challenging  Another question is a clear rule on when a smoothing by filtering is to be avoided and when a simple binary decision is to be undertaken

7. 7 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras MRF-Based Depth Up-sampling  Diebel et al., NIPS 2005  Use a multi-resolution MRF which ties together image and range data  Exploit the fact that discontinuities in range and coloring tend to co-align  Pros and cons  Robust to changes in up-sampling scale through global optimization  High computation complexity 7 MRF framework Data term Smoothness term

8. 8 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras - High Quality Depth Map Upsampling for 3D-TOF Cameras / ICCV 2011 A novel method based on MRF approach

9. 9 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Problem Definition

10. 10 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras

11. 11 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras System Setup and Preprocessing

12. 12 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras

13. 13 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Evaluation on Weighting Terms

14. 14 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras The plot of PSNR accuracy  The combined weighting term consistently produce the best results under different upsampling scale.

15. 15 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras NLM regularization term  Thin structure protection  By allowing the pixels on the same nonlocal structure to reinforce each other within a larger neighborhood.

16. 16 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras User Adjustments Additional weighting term for counting the additional depth discontinuity information is defined as: After adding the additional depth samples, our algorithm generates the new depth map using the new depth samples as a hard constraint in Equation (4)

17. 17 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Experimental Results (Synthetic)

18. 18 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Experimental Results (Real world)

19. 19 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Is this method ideal enough?  Noise distribution in depth map:  Practical depth map contains more complicated noise distribution than the Gaussian noise  Neighborhood extension to higher dimension:  Practical depth data is a sequence of successive depth maps  Spatial domain  spatial-temporal domain

20. 20 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Spatial-Temporal MRF-Based Depth Map Refinement  Zhu et al., CVPR 2008  Combine range sensor with stereo sensor  Extend the MRF to temporal domain to take the temporal coherence into account  Pros and cons  Improve accuracy by using temporal coherence  Do not consider changes of depth on time-varying 20 Spatial-temporal MRF structure Data term Smoothness term

21. 21 2013-03-26 / Computer Vision Laboratory Seminar high quality depth map upsampling for 3D-ToF cameras Summary of MRF-based approach  MRF-based approach  Maintaining sharp depth boundaries  Easy adoption of several weighting factors  Easy cooperation with user adjustment  Possible improvements in the future  Noise distribution consideration in practical depth data  Temporal smoothness consideration by neighborhood extension