3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion

Understand

Point cloud completion aims to generate a complete and high-fidelity point cloud from an initially incomplete and low-quality input.

• A prevalent strategy involves leveraging Transformer-based models to encode global features and facilitate the reconstruction process.
• However, the adoption of pooling operations to obtain global feature representations often results in the loss of local details within the point cloud.
• Moreover, the attention mechanism inherent in Transformers introduces additional computational complexity, rendering it challenging to handle long sequences effectively.