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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Improved Point Cloud Representation via a Learnable Sort-Mix-Attend Mechanism.

Yuyan Zhang1, Xi Wang2, Zhang Yi1

  • 1Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

We introduce the Sort-Mix-Attend (SMA) layer to improve deep learning on 3D point clouds. SMA enhances efficient MLP-based networks, boosting 3D classification and segmentation performance.

Keywords:
3D object classification3D segmentationcanonicalizationdeep learninglocal aggregationpoint cloudsrepresentation learningscene understanding

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Area of Science:

  • Computer Vision
  • Deep Learning
  • 3D Point Cloud Processing

Background:

  • Deep learning on 3D point clouds has advanced, with hierarchical architectures becoming prevalent.
  • Focus has been on complex operators, neglecting efficient point-wise MLP-based backbones' representational capacity.

Purpose of the Study:

  • To enhance the representational capacity of efficient point-wise MLP-based backbones for 3D point cloud analysis.
  • To introduce a novel module that imposes task-driven structure on local point sets.

Main Methods:

  • Proposing a differentiable Sort-Mix-Attend (SMA) layer that dynamically serializes neighborhoods.
  • Generating a geometric basis and employing a differentiable sorting mechanism within the SMA layer.
  • Enabling MLP-based networks to model rich feature interactions before aggregation.

Main Results:

  • Integrating SMA into PointNeXt-S improved Overall Accuracy (OA) to 88.3% on ScanObjectNN.
  • Boosting PointNet++ architecture by 5.2% in OA.
  • Achieving 86.0% OA with SMA-Tiny (0.3M parameters) for computational efficiency.

Conclusions:

  • The SMA layer effectively enhances standard backbones for 3D classification and segmentation.
  • SMA offers structural superiority and computational cost-effectiveness for real-world 3D perception.
  • The proposed method demonstrates practical significance in advancing 3D point cloud understanding.