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A Protocol for Real-time 3D Single Particle Tracking

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SC-PointLSTM: point cloud target recognition framework for linear array push-broom laser fuze.

Zhen Zheng, Bingting Zha, Chenyoushi Xu

|February 20, 2026

Summary

This summary is machine-generated.

This study introduces the SC-PointLSTM framework for rapid laser target recognition. It achieves high accuracy and speed in dynamic environments using enhanced point cloud processing and temporal fusion.

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Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Laser detection and point cloud processing are critical for autonomous systems.
High-precision target recognition demands speed and accuracy, especially in dynamic environments.

Purpose of the Study:

To develop a novel, rapid target recognition algorithm for linear array push-broom laser fuzes.
To address computational challenges in real-time dynamic environments for laser imaging fuzes.

Main Methods:

The SC-PointLSTM framework integrates an enhanced PointNet for sparse point cloud processing.
A double-layer long short-term memory (LSTM) network is used for temporal feature fusion.
Parallel processing generates 3D point clouds and fuses timestamp sequences for real-time recognition.

Main Results:

The SC-PointLSTM framework achieved an F-score exceeding 0.8.
Average recognition time was approximately 32 milliseconds.
Evaluated on simulated and real-world datasets, demonstrating robustness and efficiency.

Conclusions:

SC-PointLSTM offers a robust and scalable solution for high-speed target recognition.
The framework enables real-time, accurate target recognition in dynamic laser detection scenarios.