A Lightweight Network for Point Cloud Analysis via the Fusion of Local Features and Distribution Characteristics

Area of Science:

• Computer Vision
• Machine Learning
• 3D Data Analysis

Background:

• Integrating local features and spatial distribution is crucial for effective point cloud analysis.
• Existing methods often rely on complex feature extraction modules.
• Convolutional Neural Networks (CNNs) demonstrate the importance of spatial characteristics in feature extraction.

Purpose of the Study:

• To propose a concise architecture for integrating local features and spatial distribution in point cloud analysis.
• To develop a lightweight structure that explicitly supplements feature distribution information.
• To improve accuracy and convergence speed compared to baseline models.

Main Methods:

• Designed a lightweight architecture inspired by CNNs to integrate local features and spatial distribution.
• Employed basic shared multi-layer perceptrons (MLPs) as feature extractors.
• Introduced a novel annealing schedule optimized for snapshot ensemble technology.

Main Results:

• Achieved competitive performance on benchmark datasets (e.g., MoldeNet40 classification, S3DIS segmentation).
• Demonstrated improvements in accuracy and convergence speed over baseline models.
• The proposed model with a lightweight structure achieved state-of-the-art (SOTA) comparable results with significantly fewer parameters.

Conclusions:

• The proposed lightweight architecture effectively integrates local features and spatial distribution for point cloud analysis.
• The novel annealing schedule enhances performance when combined with snapshot ensemble techniques.
• The method offers a promising alternative to complex feature extraction modules for efficient and accurate point cloud processing.