3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion
This work addresses point cloud completion for applications like 3D modeling and computer vision, offering a novel method to improve accuracy and efficiency, though it is incremental as it builds on existing Mamba and completion techniques.
The paper tackles the problem of point cloud completion, where incomplete inputs are reconstructed into complete, high-fidelity outputs, by proposing 3DMambaComplete, a network based on the Mamba framework that outperforms state-of-the-art methods on established benchmarks.
Point cloud completion aims to generate a complete and high-fidelity point cloud from an initially incomplete and low-quality input. A prevalent strategy involves leveraging Transformer-based models to encode global features and facilitate the reconstruction process. However, the adoption of pooling operations to obtain global feature representations often results in the loss of local details within the point cloud. Moreover, the attention mechanism inherent in Transformers introduces additional computational complexity, rendering it challenging to handle long sequences effectively. To address these issues, we propose 3DMambaComplete, a point cloud completion network built on the novel Mamba framework. It comprises three modules: HyperPoint Generation encodes point cloud features using Mamba's selection mechanism and predicts a set of Hyperpoints. A specific offset is estimated, and the down-sampled points become HyperPoints. The HyperPoint Spread module disperses these HyperPoints across different spatial locations to avoid concentration. Finally, a deformation method transforms the 2D mesh representation of HyperPoints into a fine-grained 3D structure for point cloud reconstruction. Extensive experiments conducted on various established benchmarks demonstrate that 3DMambaComplete surpasses state-of-the-art point cloud completion methods, as confirmed by qualitative and quantitative analyses.