Computing the Stereo Matching Cost with CNN

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1 University at Austin Figure. The of lefttexas column displays the left input image, while the right column displays the output of our stereo method. Examples are sorted by difficulty, with easy examples appearing at the top. Some of the difficulties include reflective surfaces, occlusions, as well as regions with many jumps in disparity, e.g. fences and shrubbery. The examples towards the bottom were selected to highlight the flaws in our method and to demonstrate the inherent difficulties of stereo matching on real-world images. Computing the Stereo Matching Cost with CNN differences. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(): Tongliang Liao Guillaume Dardelet learning multiple experts behaviors. In BMVC, pages

2 Introduction Traditional Vision paper for stereo matching Plus CNN as a component followed by post-processing pipelines Not an end-to-end solution

3 Stereo Matching 2 cameras: left/right Match pixels Compute pixel-wise disparity Left input image Only horizontal Disparity -> Depth info Right input image Figure 1. The input is a pair of images from the left and right ca

4 Left input image Output disparity map Right input image 0 m 20 m 1.7 m gure 1. The input is a pair of images from the left and right camera. The two input images differ mostly in horizontal locations of obj ote that objects closer to the camera have larger disparities than objects farther away. The output is a dense disparity map shown on ght, with warmer colors representing larger values of disparity (and smaller values of depth). 3] constructed a new dataset of 30 stereo pairs and used 3.1. Creating the dataset

5 Stereo Matching Left input image Minimize energy/cost function Similarity Smoothness Right input image Figure 1. The input is a pair of images from the left and right ca

6 Architecture Left image patch Right image patch x input L1: L2: Patch-wise lower part L3: concatenate 400 L4: Cross-patch upper part L: L6: L7: L8: 2

7 Architecture x input Patch-wise lower part Left image patch Right image patch 1 Conv FC L1: No pooling L2: Cross-patch upper part L3: concatenate 400

8 Architecture x input L3: concatenate 400 Patch-wise lower part L4: Cross-patch upper part L: Pixel pair within 1xD window L6: Concat L7: FC Output (softmax) match/mismatch L8: 2

9 Architecture Left image patch Right image patch 1 Conv x input No pooling / padding L1: 7 FC Patch-wise lower part L2: ReLU L3: concatenate 400 Cross-patch upper part L4: Preprocess lower part L: L6: Feed L3 pair to upper part Decision output L7: L8: 2

10 Training x input Left image patch Right image patch Disparity ground truth L1: Patch-wise lower part L2: Relax disparity L3: concatenate 400 Cross-patch upper part mark close answer as L4: correct L: L6: Decision output L7: L8: 2

11 Initial Cost x input Left image patch Right image patch L1: 1 output vector per pixel Patch-wise lower part L2: L3: concatenate Cross-patch upper part L4: L: Decision output L6: L7: L8: 2

12 Segmentation top arm q horizontal arms of q [±d, ±d] window Similar color left arm p l p right arm bottom arm re 3. The support region for position p, is the union of

13 Segmentation [±d, ±d] window top arm Similar color q horizontal arms of q For each pixel pair take intersection left arm p l p right arm average cost Repeat 4 times. for blurry boundary bottom arm re 3. The support region for position p, is the union of

14 Minimization top arm q horizontal arms of q Minimize energy I: input image D: disparity left arm p l p right arm Cost + f( I, D) bottom arm re 3. The support region for position p, is the union of

15 Minimization Minimize energy top arm I: input image D: disparity q horizontal arms of q Cost + f( I, D) left arm p l p right arm DP along x/y average results only semi-optimal bottom arm re 3. The support region for position p, is the union of

16 Consistency Remove inconsistent regions linear interpolate top arm Smooth the disparity quadratic interpolate q horizontal arms of q left arm p l p right arm Fill boundary copy boundary pixels blur median + gaussian within shape bottom arm re 3. The support region for position p, is the union of

17 Result 14 grayscale images 4M patches Training: h Prediction: 0.01 fps (% time on CNN) 2.61% error

18 Pros

19 Accuracy rforming stereo algorithms on this dataset. Method Error MC-CNN This paper 2.61 % SPS-StFl Yamaguchi et al. [20] 2.83 % Best at that time VC-SF Vogel et al. [16] 3.0 % CoP Anonymous submission 3.30 % SPS-St Yamaguchi et al. [20] 3.3 % PCBP-SS Yamaguchi et al. [1] 3.40 % Current best: PSMNet: 1.61% DDS-SS Anonymous submission 3.83 % StereoSLIC Yamaguchi et al. [1] 3.2 % PR-Sf+E Vogel et al. [17] 4.02 % PCBP Yamaguchi et al. [18] 4.04 % The KITTI stereo leaderboard as it stands in November

20 Performance Profile Component Convolutional neural network Semiglobal matching Cross-based cost aggregation Everything else Runtime s 3 s 2 s 0.03 s PSMNet: 1.61% / 1.3s GC-NET: 1.77% / 0.s

21 CUDA Implementation Component Convolutional neural network Semiglobal matching Cross-based cost aggregation Everything else Runtime s 3 s 2 s 0.03 s CUDA for stereo algorithms

22 Data Size Vary the training set Linear boost Error 3.6 % 3.6 % 3. % 3. % 3.4 % 3.4 % 3.3 % 3.3 % 3.2 % Number of training stereo pairs ure 4. The error on the test set as a function of the number

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