Stereo Matching Information Permeability For Stereo Matching – Cevahir Cigla and A.Aydın Alatan – Signal Processing: Image Communication, 2013 Radiometric Invariant Stereo Matching Based On Relative Gradients – Xiaozhou Zhou and Pierre Boulanger – International Conference on Image Processing (ICIP), IEEE

Outline Introduction Related Works Methods Conclusion 2

Introduction Goal – Get accurate disaprity maps effectively. – Find more robust algorithm, especially refinement technique. Foucus : Refinement step and Comparison 3

Related Works Stereo Matching – The same object, the same disparity Segmentation Calculate correspond pixels similarity (color and geographic distance) – Occlusion handling Refinement 4

Related Works Global Methods – Energy minimization process (GC,BP,DP,Cooperative) – Per-processing – Accurate but slow Local Methods – A local support region with winner take all – Fast but inaccurate. – Adaptive Support Weight 5

Related Works Disparity Refinement Disparity Optimization Cost Aggregation Matching Cost Computation 6 Local methods algorithm [1] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision (IJCV), 47:7–42, 2002.

Edge Preserving filter : Remove noise and preserve structure/edge, like object consideration. Adaptive Support Weight [3] Bilateral filter(BF) [34] Guided filter(GF) [5] Geodesic diffusion [33] Arbitrary Support Region [39] Related Works 7

Reference Papers [3] Kuk-JinYoon, InSoKweon, Adaptive support weight approach for correspondence search, IEEE Transactions on Pattern Analysis and Machine Intelligence, [5] C. Rhemann, A. Hosni, M. Bleyer, C. Rother, M. Gelautz, Fast cost-volume filtering for visual correspondence and beyond, CVPR [33] L. De-Maetzu, A. Villanueva nad, R. Cabeza, Near real-time stereo matching using geodesic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, [34] A. Ansar, A. Castano, L. Matthies, Enhanced real time stereo using bilateral filtering, in: Proceedings of the International Symposium on 3D Data Processing Visualization and Transmission, [39] X. Mei, X Sun, M Zhou, S. Jiao, H. Wang, Z. Zhang, On building an accurate stereo matching system on graphics hardware, in: Proceed- ings of GPUCV

Information Permeability For Stereo Matching Method A. 9

Methods A. Goal : Get high quality but low complexity Save memory Real-time application Successive Weighted Summation (SWS) – Constant time filtering + Weighted aggregation 10 ◎ Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE March

Methods A. Cost Computation 11

Census Transform X Census transform window :

Census Hamming Distance Left image Right image Hamming Distance = XOR

Methods A. Cost Computation 14

Methods A. 15 Cost Aggregation

Methods A. Cost Aggregation 16

Methods A. 17 (b)Horizontal effective weights (c)Vertical effective weights(d)2D effective weights

18 (a) AW [3] (b) Geodesic support [12] (c) Arbitrary support region [4] (d) Proposed Comparison With Other Methods

Methods A. Refinement – Using cross-check to detect reliable and occluded region detection 19 ф is a constant (set to 0.1 throughout experiments)

Methods A. 20 (a)Linear mapping function for reliable pixels based on disparities (b)The resultant map for the left image

Disparity Variation 21 Before  After

22 (b) Without occlusion handling, bright regions correspond to small disparities (c) Detection of occluded and un-reliable regions

Methods A. 23 (b) occlusion handling with no background favoring (c) the proposed occlusion handling

Experimental Results A. 24

25

Parameter of Method A. 26

27

Experimental Results A. 28

Experimental Results A. 29 6D + 4D * V.S. 129D + 21D * 10~15X

Experimental Results A. 30 Proposed method is the fastest method without any special hardware implementation among Top-10 local methods of the Middlebury test bench, as of February 2013.

31 Proposed O(1) AW Guided filter Geodesic support Arbitrary shaped cross filter

Experimental Results A. 32

Computational times A. 33

Error Analysis A. 34

Comparison with Full-Image ◎ Full-ImageProposed InitializationAD + GradientSAD + Census Aggregation Refinement1.Cross checking (lowest disparity) 2.Weighted median filter 1. Cross checking (normalized disparity) 2. Median filter (background handling) 35 ◎ Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE

Comparison with Full-Image 36

37 Full-Image Results

38 Full-Image Results Proposed Results Ground Truth

Comparison with Full-Image My Experimental Results (SAD+Gradient) Lowest V.S. Normalized disparity 39

Radiometric Invariant Stereo Matching Based On Relative Gradients Method B. 40

Methods B. Goal : Adapt different environmental factors.(Illumination condition) Effective and robust algorithm Relative gradient algorithm + Gaussian weighted function 41

Background Lighting Model : – View independent, body reflection 42

Background Lighting Model : 43 ANCC

Method B. Cost Computation – 44 (i,j)

Method B. Cost Aggregation – Refinement – – Avoid White and black noises 45

Experimental Results B. 46

47

Experimental Results B. 48

Experimental Results B. 49

Experimental Results B. My Experimental Results (SAD+Gradient) Original V.S.Rerange disparity 50

Experimental Results B. Using related gradient intialization 51

Conclusion Initialization ADc/SADcADgC-CensusG-Census??? Aggregation Weighted-WindowPermeabilityCost-Filter Arbitrary Support Region ??? Refinement Lowest NeighborNormalizesRe-RangeScan-line??? 52