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Point Cloud Completion Network Based on Multi-Dimensional Adaptive Feature Fusion and Informative Channel Attention

Di Tian1, Jiahang Shi1, Jiabo Li1

  • 1Mechanical Engineering College, Xi'an Shiyou University, Xi'an 710065, China.

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Summary
This summary is machine-generated.

This study introduces a new method for point cloud completion, improving 3D data integrity by effectively reconstructing local details and global features. The novel network enhances fine-detail reconstruction for practical 3D perception applications.

Keywords:
attention mechanismdetail reconstructionfeature refinementpoint cloud completion

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Area of Science:

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

Background:

  • Point cloud data is crucial for 3D perception but often contains holes due to device limitations and environmental factors.
  • Existing point cloud completion methods often prioritize global features, neglecting local structural details and fine-grained control.
  • This leads to incomplete data and reduced integrity in the reconstructed point clouds.

Purpose of the Study:

  • To address the limitations of current point cloud completion techniques.
  • To develop a method that effectively captures both local and global features for improved detail reconstruction.
  • To enhance the overall feature representation and completion accuracy of point cloud data.

Main Methods:

  • Proposed a Set Combination Multi-Layer Perceptron (SCMP) module for simultaneous local and global feature extraction.
  • Introduced a Squeeze Excitation Pooling Network (SEP-Net) module for adaptive channel attention and feature enhancement.
  • Developed a Feature Fusion Point Fractal Network (FFPF-Net) integrating these modules for multi-dimensional feature fusion and progressive refinement.

Main Results:

  • FFPF-Net demonstrated significant improvements over L-GAN and PCN on ShapeNet-Part and MVP datasets, with average prediction error reductions of 1.3 and 1.4.
  • Achieved average completion errors of 0.783 on ShapeNet-Part and 0.824 on MVP, showcasing enhanced fine-detail reconstruction.
  • The method effectively addresses the loss of local detail information and improves data integrity.

Conclusions:

  • The proposed FPFF-Net effectively enhances point cloud completion performance by integrating local and global feature extraction with attention mechanisms.
  • The network's ability to reconstruct fine details improves data integrity, making it suitable for practical 3D perception tasks.
  • This advancement promotes the wider application of point cloud data in various real-world scenarios.