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Shuangmin Chen1, Taijun Liu1, Zhenyu Shu2

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Summary
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We introduce an improved shape diameter function (SDF) by offsetting the 3D object. This method enhances robustness to noise and geometric details, significantly speeding up computation for 3D shape analysis.

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Geometric Modeling

Background:

  • The shape diameter function (SDF) is crucial for 3D shape analysis, segmentation, and retrieval due to its pose-invariant nature.
  • Traditional SDF computation is computationally intensive and sensitive to noise and geometric details in real-world 3D models.
  • Existing methods struggle with non-watertight surfaces, limiting their applicability.

Purpose of the Study:

  • To develop a more robust and efficient method for computing the shape diameter function.
  • To enhance the applicability of SDF to real-world 3D models, including those with noise and gaps.
  • To improve the performance of SDF in applications like shape retrieval and segmentation.

Main Methods:

  • A novel approach defining SDF by offsetting the input 3D object surface.
  • Utilizing the offset surface to obtain reliable normal vectors for robust computation.
  • Computing penetration distance along a single direction for computational efficiency.

Main Results:

  • The offset-surface based SDF demonstrates robustness to noise and insensitivity to geometric details.
  • The proposed method achieves approximately 10 times faster computation compared to existing SDF algorithms.
  • The enhanced SDF shows significant improvements in shape retrieval and segmentation tasks.

Conclusions:

  • Offsetting the 3D object surface provides a robust and efficient way to compute SDF.
  • This improved SDF is suitable for a wider range of 3D models, including those with defects.
  • The method offers a substantial advancement for 3D shape analysis applications.