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Learning global-view correlation for salient object detection in 3D point clouds

  • 0College of Information Engineering, Shanghai Maritime University, No. 1550 Haigang Avenue, Shanghai, 201306, China.
Neural Networks : the Official Journal of the International Neural Network Society +

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September 12, 2025

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September 12, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces the Saliency Filtration Network (SFN) for 3D salient object detection (SOD) in point clouds. SFN effectively refines saliency by understanding global scene context and purifying common correlations.

Area Of Science

  • Computer Vision
  • 3D Data Analysis
  • Artificial Intelligence

Background

  • Salient object detection (SOD) in 3D point clouds is challenging due to data irregularity.
  • Existing methods often miss crucial scene-level global-view correlations.
  • Effective saliency prediction requires understanding both local and global context.

Purpose Of The Study

  • To develop a novel approach for salient object detection in 3D point clouds.
  • To address the limitations of existing methods by incorporating global-view scene understanding.
  • To introduce a method that refines saliency representations by filtering scene-common correlations.

Main Methods

  • Proposed the Saliency Filtration Network (SFN) with a two-stage strategy.
  • Introduced the Residual Relation-aware Transformer (RRT) module for long-range context aggregation.
  • Developed the Global Bilinear Correlation based Filtration (GBCF) module for saliency purification.

Main Results

  • SFN effectively isolates salient objects by refining representations from global scene correlations.
  • The RRT module captures long-range dependencies inspired by human visual perception.
  • The GBCF module establishes dense correlations for accurate saliency purification.

Conclusions

  • The proposed SFN method achieves state-of-the-art accuracy in 3D salient object detection.
  • SFN significantly outperforms existing methods on the PCSOD benchmark.
  • This work advances SOD in point clouds by leveraging global-view scene understanding.

Keywords:

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