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

2026-02-28

AI Summary

Existing point cloud completion methods struggle to balance reconstruction quality and computational efficiency. This work proposes a parallel point cloud completion framework that first structures unordered point clouds into ordered subsets guided by principal component analysis (PCA), then employs a hybrid architecture combining Mamba-based sequence modeling with a Transformer multi-head decoder to enable efficient parallel reconstruction. The proposed approach significantly improves point distribution uniformity and detail fidelity, achieving state-of-the-art performance across multiple benchmarks—including PCN, ShapeNet-55/34, and KITTI—while maintaining a favorable trade-off between accuracy and efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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 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.

Problem

• point cloud completion
• computational efficiency
• reconstruction quality
• unordered point clouds
• parallel processing

Innovation

• Point Cloud Completion
• Mamba-Transformer Hybrid
• Parallel Reconstruction
• PCA-guided Structuring
• Efficient 3D Generation