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Efficient and lightweight long-range modeling for 3d point cloud classification and segmentation
Dongzhen Liu1, Yuzhong Deng1, Jianxiao Zou1,2
1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichaun, China.
Plos One
|July 16, 2026
Summary
Point BiLSTM efficiently processes 3D point clouds by capturing global context with its novel bidirectional long short-term memory (BiLSTM) module. This framework offers faster inference and reduced computational cost for computer vision tasks.
Area of Science:
- Computer Vision
- Robotics
- Intelligent Systems
Background:
- 3D point clouds are crucial for visual understanding but existing methods struggle with global context and high computational costs.
- Current models often prioritize local geometric details, limiting their ability to capture long-range dependencies in 3D data.
Purpose of the Study:
- Introduce Point BiLSTM, an efficient and lightweight framework for 3D point cloud classification and segmentation.
- Address limitations in capturing global context and high computational demands of existing 3D point cloud processing methods.
Main Methods:
- Propose a bidirectional long short-term memory (BiLSTM)-based sequencer module for linear-complexity modeling of long-range contextual dependencies.
- Introduce a Mixed Sequence Soft Cross-Entropy Loss to enhance robustness against permutation ambiguity in unordered point clouds.
Main Results:
- Point BiLSTM achieves competitive performance on ModelNet40, ScanObjectNN, and ShapeNet Part benchmarks.
- Demonstrates the fastest inference speed, outperforming state-of-the-art methods by up to 54.2% on real-world datasets.
- Significantly reduces computational complexity and memory consumption compared to existing approaches.
Conclusions:
- Point BiLSTM provides an effective solution for efficient 3D point cloud learning, balancing performance with low computational cost.
- The proposed BiLSTM module and loss function enhance global feature learning and robustness in 3D point cloud analysis.
