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SPU+: Dimension Folding for Semantic Point Cloud Upsampling

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society +

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May 26, 2025

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May 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Semantic Point Cloud Upsampling (SPU) with SPU+ introduces dimension folding to improve 3D point cloud reconstruction. This novel approach enhances feature representation, achieving state-of-the-art performance in upsampling tasks.

Area Of Science

  • Computer Vision
  • 3D Data Processing
  • Machine Learning

Background

  • Semantic Point Cloud Upsampling (SPU) reconstructs dense 3D point clouds from sparse inputs.
  • Conventional methods face a dimensional bottleneck with high-dimensional feature vectors.
  • Effective feature representation is crucial for semantic tasks in SPU.

Purpose Of The Study

  • To propose a novel SPU method, SPU+, that addresses the dimensional bottleneck.
  • To introduce dimension folding as an alternative strategy for handling high-dimensional features.
  • To achieve state-of-the-art performance in semantic point cloud upsampling.

Main Methods

  • SPU+ decomposes high-dimensional features into g-dimensional packages for interaction.
  • A 3D Residual Graph Convolution Block (3D-RGCB) enables efficient 3D package convolutions.
  • A scaling-and-shuffling strategy is developed for large-scale upsampling.

Main Results

  • Dimension folding proves effective in enhancing feature representation for SPU.
  • SPU+ achieves state-of-the-art performance on publicly available datasets.
  • Analysis of covering number demonstrates advantages of the 3D package representation.

Conclusions

  • SPU+ offers a significant advancement in Semantic Point Cloud Upsampling.
  • Dimension folding and 3D package representation are key innovations.
  • The proposed method demonstrates superior performance and efficiency.