LiDAR Point Cloud Upsampling with Mamba Architecture

This paper presents a range-image-based LiDAR point cloud upsampling method, utilizing the State Space Model (SSM) framework. The proposed method significantly reduces computational demands while maintaining high performance, improving efficiency in LiDAR point cloud upsampling tasks. We validated the effectiveness of our approach through experiments on the KITTI dataset. Results demonstrate that the SSM-based LiDAR upsampling method achieves a 69% reduction in FLOPs and a 72% decrease in number of parameters compared to the Swin Transformer model. This efficient solution offers enhanced LiDAR point cloud resolution, making it a promising approach for cost-effective applications in autonomous driving and related fields.