CompletionMamba: Taming State Space Model for Point Cloud Completion.

Area of Science:

• Computer Vision
• 3D Shape Reconstruction
• Deep Learning

Background:

• Point cloud completion is vital for reconstructing 3D shapes from incomplete data.
• Transformers excel at global dependencies but struggle with computational costs for long sequences.
• State Space Models (SSMs) offer memory efficiency for long sequences but face challenges with unordered point clouds due to causality requirements.

Purpose of the Study:

• To develop a novel deep learning network for efficient and accurate point cloud completion.
• To address the limitations of existing methods in capturing complex 3D spatial relationships and shape information.

Main Methods:

• Introduced CompletionMamba, a State Space Model (SSM)-based network for point cloud completion.
• Developed a method to causally structure point clouds by rearranging coordinates and defining local neighborhood spaces.
• Integrated shape codes into the Mamba model to enable shape information propagation for comprehensive modeling.

Main Results:

• CompletionMamba effectively captures both global and local dependencies within point clouds.
• The proposed shape-aware Mamba significantly enhances the modeling of complete 3D shapes.
• Achieved state-of-the-art performance on the MVP and PCN datasets for point cloud completion tasks.

Conclusions:

• CompletionMamba offers a powerful and efficient solution for 3D point cloud completion.
• The integration of SSMs with shape-aware mechanisms represents a significant advancement in the field.
• This method demonstrates superior performance in reconstructing complete 3D shapes from partial scans.