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
  • Introduction
  • Related Works
  • Proposed Method
    • Weighted Joint Bilateral Filter
    • Slope Depth Compensation Filter
  • Experimental Results
  • Conclusion
introduction1
Introduction
  • Goal : Using two filters to get more accurate disparity map in real-time.
  • Consideration
    • Noise reduction
    • Correct edges
    • Blurring control

Goal

related works1
Related Works
  • Stereo Matching

Left Image Right Image

related works3
Related Works
  • Flow Chart (Local)
  • Disparity Map Refinement
related works4
Related Works
  • Depth map refinement with filter
    • Median filter
    • Bilateral filter

Filter

Input depth map

Output depth map

related works5
Related Works
  • 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
  • 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
  • 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 method1
Proposed Method
  • Weighted joint bilateral filter
    • Noise reduction
    • Edge correction
  • Slope depth compensation filter
    • Blurring control
weighted joint bilateral filter
Weighted 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
  • 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
  • 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
  • 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 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 Depth Compensation Filter
  • X in Dx∈ {INITIAL;WJBF;SDCF}
experimental results1
Experimental Results
  • 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 results4
Experimental Results
  • 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 results6
Experimental Results
  • Device : Intel Core i7-920 2.93GHz
  • Comparing running time (ms) of BM plus proposed filter with selected stereo methods.
experimental results8
Experimental Results
  • Use the proposed filter for depth maps from Microsoft Kinect.
conclusion1
Conclusion

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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