GeoSeqNet: A Geometry-Aware Sequential Network for Robust 3D Point Cloud Analysis

Area of Science:

• Computer Vision
• Geometric Deep Learning
• Robotics

Background:

• 3D point cloud data is crucial for remote sensing, robotics, and scene analysis.
• Real-world data challenges include noise, incomplete geometry, occlusion, and irregular sampling.
• Current methods struggle with robustness and efficiency due to fixed aggregation or costly global interactions.

Purpose of the Study:

• To propose GeoSeqNet, a novel framework for robust 3D point cloud analysis.
• To address limitations in geometric representation and long-range contextual modeling.
• To improve performance under complex sensing conditions.

Main Methods:

• Introduced an Enhanced Local Operator (ELO) for stronger local geometric representation.
• Employed a Geometric Encoding Module (GEM) to preserve spatial information during feature interactions.
• Designed an Adaptive Gate Fusion (AGF) module for efficient long-range contextual modeling using LSTM and GRU.

Main Results:

• GeoSeqNet achieves robust feature learning with low computational overhead.
• Demonstrated effectiveness on ModelNet40, ScanObjectNN, and ShapeNetPart datasets.
• Exhibited a favorable efficiency-accuracy trade-off and strong robustness in complex scenarios.

Conclusions:

• GeoSeqNet provides an effective and reliable solution for 3D point cloud understanding.
• The framework successfully integrates local geometric cues and long-range contextual information.
• GeoSeqNet offers significant improvements for challenging sensing environments.