PPC-MT: Parallel Point Cloud Completion with Mamba-Transformer Hybrid Architecture

Researchers propose PPC-MT, a hybrid Mamba-Transformer architecture for point cloud completion that uses parallel processing guided by Principal Component Analysis. The framework outperforms existing methods on benchmark datasets while maintaining computational efficiency by combining Mamba's linear complexity with Transformer's fine-grained modeling capabilities.

• →PPC-MT introduces a novel parallel framework for point cloud completion using hybrid Mamba-Transformer architecture.
• →The method employs Principal Component Analysis to transform unordered point clouds into structured, ordered sets for parallel reconstruction.
• →The hybrid approach combines Mamba's efficient linear complexity for encoding with Transformer's detailed sequence modeling for decoding.
• →Extensive testing on PCN, ShapeNet-55/34, and KITTI datasets shows superior performance compared to state-of-the-art methods.
• →The framework achieves improved point distribution uniformity and detail fidelity while preserving computational efficiency.