[2603.00870] PPC-MT: Parallel Point Cloud Completion with Mamba-Transformer Hybrid Architecture

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00870 (cs) [Submitted on 1 Mar 2026] Title:PPC-MT: Parallel Point Cloud Completion with Mamba-Transformer Hybrid Architecture Authors:Jie Li, Shengwei Tian, Long Yu, Xin Ning View a PDF of the paper titled PPC-MT: Parallel Point Cloud Completion with Mamba-Transformer Hybrid Architecture, by Jie Li and 3 other authors View PDF HTML (experimental) Abstract: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 qual...