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Adaptive deep learning-based neighborhood search method for point cloud.

Qian Xiang1, Yuntao He2, Donghai Wen3

  • 1School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China.

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This study introduces a learnable neighborhood search for deep learning in 3D vision. The method adaptively selects search strategies, improving point cloud processing performance and model efficiency.

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Area of Science:

  • Computer Vision
  • 3D Deep Learning
  • Geometric Deep Learning

Background:

  • Point cloud processing is challenging due to unstructured data.
  • Deep learning models are increasingly used for point cloud tasks like recognition and segmentation.
  • Neighborhood search is a critical component affecting deep learning model performance.

Purpose of the Study:

  • To develop a novel learnable neighborhood search method for point cloud processing.
  • To enable adaptive selection of search strategies based on point characteristics.
  • To enhance the performance and efficiency of deep learning models in 3D vision.

Main Methods:

  • Proposed a learnable neighborhood search algorithm.
  • Implemented adaptive selection of search methods per point.
  • Integrated the method into existing point cloud deep learning architectures.

Main Results:

  • Validated on ModelNet40 and ShapeNetPart datasets.
  • Achieved performance improvements across tested models.
  • Demonstrated a maximum performance gain of 1.1%.

Conclusions:

  • The learnable neighborhood search method effectively improves point cloud processing.
  • The plug-and-play nature allows easy integration into existing deep learning frameworks.
  • This approach offers a flexible and efficient solution for 3D vision tasks.