Weighted joint bilateral filter with slope depth compensation filter for depth map refinement
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Weighted Joint Bilateral Filter with Slope Depth Compensation Filter for Depth Map Refinement. Takuya Matsuo, Norishige Fukushima and Yutaka Ishibashi VISAPP 2013 International Conference on Computer Vision Theory and Application. Outline. Introduction Related Works Proposed Method

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Weighted joint bilateral filter with slope depth compensation filter for depth map refinement

Weighted Joint Bilateral Filter with Slope Depth Compensation Filter for Depth Map Refinement

Takuya Matsuo, Norishige Fukushima and Yutaka Ishibashi

VISAPP 2013

International Conference on Computer Vision Theory and Application


Outline
Outline Compensation Filter

  • Introduction

  • Related Works

  • Proposed Method

    • Weighted Joint Bilateral Filter

    • Slope Depth Compensation Filter

  • Experimental Results

  • Conclusion


Introduction
Introduction Compensation Filter


Introduction1
Introduction Compensation Filter

  • Goal : Using two filters to get more accurate disparity map in real-time.

  • Consideration

    • Noise reduction

    • Correct edges

    • Blurring control

Goal


Related works
Related Works Compensation Filter


Related works1
Related Works Compensation Filter

  • Stereo Matching

Left Image Right Image


Related works2
Related Works Compensation Filter


Related works3
Related Works Compensation Filter

  • Flow Chart (Local)

  • Disparity Map Refinement


Related works4
Related Works Compensation Filter

  • Depth map refinement with filter

    • Median filter

    • Bilateral filter

Filter

Input depth map

Output depth map


Related works5
Related Works Compensation Filter

  • Bilateral filter

    • Space weight:Near pixels has large weight

    • Color weight:Similar color pixels has large weight

  • Smoothing

    • Keep edges

    • Weak in spike noise


Related works6
Related Works Compensation Filter

  • Joint bilateral filter

    • Add in the reference image

    • Color weight is calculated by the reference

    • Keep object edges of the reference

Reference : Low noise Target : High noise Filtered image


Related works7
Related Works Compensation Filter

  • Joint bilateral filter

    • Noise reduction O

    • Correct edge O

    • Blurring X

      • Mixed depth values

      • Spreading error regions

  • Multilateral filter

    • Space + Color + Depth

    • Boundary recovering X


Proposed method
Proposed Method Compensation Filter


Proposed method1
Proposed Method Compensation Filter

  • Weighted joint bilateral filter

    • Noise reduction

    • Edge correction

  • Slope depth compensation filter

    • Blurring control


Weighted joint bilateral filter
Weighted Compensation Filter Joint Bilateral Filter

  • 𝐷: Depth value

  • 𝑝: Coordinate of current pixel

  • 𝑠: Coordinate of support pixel

  • 𝑁: Aggregation set of support pixel

  • 𝑤(),𝑐(): Space/color weight

  • 𝜎𝑠,𝜎𝑐: Space/color Gaussian distribution

  • 𝑅𝑠: Weight map


Weighted joint bilateral filter1
Weighted Joint Bilateral Filter Compensation Filter

  • Add in the weight map

    • Controlling amount of influence on a pixel

    • Weight of the edge and error is small

Joint bilateral filter

- Mixed depth values

- Spreading error regions


Weighted joint bilateral filter2
Weighted Joint Bilateral Filter Compensation Filter

  • Making weight map

    • Space/color/disparity weight

    • Sum of nearness of space, color, and disparity between center pixel and surrounding pixels.


Weighted joint bilateral filter3
Weighted Joint Bilateral Filter Compensation Filter

  • Mask image is made by Speckle Filter

    • Detecting speckle noise

    • Weight of speckle region is 0

Red region: speckle noise

Weight = 0



Slope depth compensation filter
Slope Compensation Filter Depth Compensation Filter

  • Weighted joint bilateral filter

    • Remaining small blurring

    • Difference between foreground and background color is small

  • Slope depth compensation filter

    • Reason of blurring is mixed depth value

    • Convert mixed value to non-blurred candidate using initial depth map

Removing remaining blur


Slope depth compensation filter1
Slope Compensation Filter Depth Compensation Filter

  • X in Dx∈ {INITIAL;WJBF;SDCF}


Slope depth compensation filter2
Slope Compensation Filter Depth Compensation Filter


Proposed method2
Proposed Method Compensation Filter


Experimental results
Experimental Results Compensation Filter


Experimental results1
Experimental Results Compensation Filter

  • Evaluating accuracy improvement for various types of depth maps

    • Block Matching (BM)

    • Semi-Global Matching (SGM)

    • Efficient Large-Scale (ELAS)

    • Dynamic Programing (DP)

    • Double Belief Propagation (DBP)


Experimental results2
Experimental Results Compensation Filter


Experimental results3
Experimental Results Compensation Filter


Experimental results4
Experimental Results Compensation Filter

  • Comparing proposed method with cost volume refinement(Teddy).

32 times slower

Yang, Q., Wang, L., and Ahuja, N. A constantspace belief propagation algorithm for stereo matching.

In Computer Vision and Pattern Recognition(2010).


Experimental results5
Experimental Results Compensation Filter


Experimental results6
Experimental Results Compensation Filter

  • Device : Intel Core i7-920 2.93GHz

  • Comparing running time (ms) of BM plus proposed filter with selected stereo methods.


Experimental results7
Experimental Results Compensation Filter


Experimental results8
Experimental Results Compensation Filter

  • Use the proposed filter for depth maps from Microsoft Kinect.


Conclusion
Conclusion Compensation Filter


Conclusion1
Conclusion Compensation Filter

Contribution

  • The proposed methods can reduce depth noise and correct object boundary edge without blurring.

  • Amount of improvement is large when an input depth map is not accurate.

    Future Works

  • Investigating dependencies of input natural images and depth maps.


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