3DMambaComplete: Structured State Space Model for High-Efficiency Point Cloud Completion

Point cloud completion seeks to reconstruct a complete and high-fidelity point cloud from an incomplete and low-quality input. Current methods predominantly rely on Transformer architectures for feature extraction. However, these approaches face two major limitations, including the computational complexity associated with the attention mechanism and the potential loss of fine-grained details during pooling operations. These issues hinder their performance on large-scale and highly fragmented point clouds. To overcome these challenges, we propose 3DMambaComplete, a novel point cloud completion method based on the selective State Space Model (SSM), particularly leveraging the Mamba architecture. Unlike traditional Transformer-based methods, 3DMambaComplete utilizes Mamba’s linear-time complexity to efficiently extract global features with significantly reduced computational overhead. Furthermore, we introduce the concepts of discriminative nodes, referred to as hyperpoints, along with dynamic offsets, to improve reconstruction quality. Specifically, the HyperPoint Generation Module encodes the downsampled features of the point cloud using the Mamba Encoder, producing a set of hyperpoints that capture critical information. Subsequently, the HyperPoint Spread Module disperses these hyperpoints across various spatial locations employing dynamic offsets to mitigate aggregation. Finally, the Point Deformation Module implements a deformation technique to transform the 2D mesh into a detailed 3D structure, resulting in high-quality point cloud completions. Experiments on widely used benchmark datasets show that 3DMambaComplete outperforms existing point cloud completion techniques in both quantitative and qualitative evaluations.