  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. To address these
issues, we propose 3DMambaComplete, a point cloud completion network built on
the novel Mamba framework. It comprises three modules: HyperPoint Generation
encodes point cloud features using Mamba's selection mechanism and predicts a
set of Hyperpoints. A specific offset is estimated, and the down-sampled points
become HyperPoints. The HyperPoint Spread module disperses these HyperPoints
across different spatial locations to avoid concentration. Finally, a
deformation method transforms the 2D mesh representation of HyperPoints into a
fine-grained 3D structure for point cloud reconstruction. Extensive experiments
conducted on various established benchmarks demonstrate that 3DMambaComplete
surpasses state-of-the-art point cloud completion methods, as confirmed by
qualitative and quantitative analyses.
