Multi-stage refinement network for point cloud completion based on geodesic attention.

Area of Science:

• Computer Vision
• Geometric Deep Learning

Background:

• Attention mechanisms have advanced point cloud tasks, but struggle with disordered, non-Euclidean data.
• Existing methods often use Euclidean or local geometry, failing to capture intrinsic point cloud characteristics.

Purpose of the Study:

• To propose a novel geodesic attention-based transformer network for point cloud completion.
• To accurately represent and leverage the non-Euclidean geometry inherent in point cloud data.

Main Methods:

• Developed a geodesic attention mechanism to capture long-range dependencies on the manifold.
• Introduced a Position Feature Extractor to enhance geometric features and non-Euclidean properties.
• Utilized a Recurrent Information Aggregation Unit for multi-stage feature refinement.

Main Results:

• The proposed network aligns feature dimensions and captures geometric dependencies effectively.
• Enhanced geometric features and non-Euclidean properties improve point cloud representation.
• The method demonstrates strong competitiveness against state-of-the-art approaches.

Conclusions:

• The novel geodesic attention transformer network offers a robust solution for point cloud completion.
• Accurately modeling non-Euclidean geometry is crucial for advanced point cloud processing.