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Advances in 3D Object Recognition: A Comparative Study of Depth Methodologies

This presentation explores innovative methods for 3D object recognition, focusing on comparative thresholds in detecting objects with low false positives and negatives. We assess the effectiveness of our method against Pedro's method and emphasize training human models in 3D. Key topics include depth descriptor optimization, the use of invariant surface characteristics, and the importance of mean and Gaussian curvature in recognizing object surfaces. Additionally, we discuss noise reduction techniques and the significance of saving images in lossless formats to maintain detail accuracy.

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Advances in 3D Object Recognition: A Comparative Study of Depth Methodologies

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  1. Presentation 4 Zach Robertson

  2. Our method Pedro’s method Low threshold, false positive Low threshold, no false positive High threshold, false negative

  3. Head Detector Our method Pedro’s Method

  4. Train Human Model in 3D • We need a good descriptor for depth as good as HOG for RGB • What should we use for Depth?

  5. Papers • Invariant Surface Characteristics for 3d Object Recognition by Besl and Jain • Mean curvature and Gaussian Curvature as visible invariant

  6. Gaussian Curvature • Gives where surface is convex, saddle, or flat • Indicates surface shape at a pixel Mean Curvature • The average of the principal curvatures • If zero, minimal surface

  7. Coded • Produce normal vectors • Produce mean curvature • Produce gaussian curvature

  8. Train SVM • There are 9 different possibilities • Only 8 will actually happen • Created a histogram of curvature

  9. Noise

  10. Normal and Curvatures Norm in Z direction Norm in X direction Norm in Y direction Mean Curvature Gaussian Curvature

  11. Fixing Noise • Use the median to smooth • Save images in lossless format (such as .png) • Changing the range of values from 0 to 255 to 0 to 4000 • Allows more detail to be maintain

  12. Median Smoothed Gaussian Smoothed

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