PPC-MT: Parallel Point Cloud Completion with Mamba-Transformer Hybrid Architecture
This addresses computational bottlenecks in 3D vision tasks like autonomous driving and robotics, though it appears incremental as it builds on existing Transformer and Mamba architectures.
The paper tackles the problem of balancing high-quality reconstruction with computational efficiency in point cloud completion by proposing PPC-MT, a parallel framework using a hybrid Mamba-Transformer architecture. The result shows that PPC-MT outperforms state-of-the-art methods on benchmark datasets like PCN, ShapeNet-55/34, and KITTI across multiple metrics.
Existing point cloud completion methods struggle to balance high-quality reconstruction with computational efficiency. To address this, we propose PPC-MT, a novel parallel framework for point cloud completion leveraging a hybrid Mamba-Transformer architecture. Our approach introduces an innovative parallel completion strategy guided by Principal Component Analysis (PCA), which imposes a geometrically meaningful structure on unordered point clouds, transforming them into ordered sets and decomposing them into multiple subsets. These subsets are reconstructed in parallel using a multi-head reconstructor. This structured parallel synthesis paradigm significantly enhances the uniformity of point distribution and detail fidelity, while preserving computational efficiency. By integrating Mamba's linear complexity for efficient feature extraction during encoding with the Transformer's capability to model fine-grained multi-sequence relationships during decoding, PPC-MT effectively balances efficiency and reconstruction accuracy. Extensive quantitative and qualitative experiments on benchmark datasets, including PCN, ShapeNet-55/34, and KITTI, demonstrate that PPC-MT outperforms state-of-the-art methods across multiple metrics, validating the efficacy of our proposed framework.