FPM-SSD: Fast Parallel Multi-Scale Smooth Signed Distance Surface Reconstruction

Abstract

Smooth signed distance surface reconstruction remains a popular technique for generating watertight surfaces from discrete point clouds. However, it frequently encounters issues with geometric detail loss when reconstructing complicated models. In this paper, we introduce a novel reconstruction technique for multi-scale smooth signed distance surfaces based on Gaussian curvature. Initially, the point cloud data is fitted using the moving least squares to calculate the Gaussian curvature. After that, a curvature-adaptive octree is constructed based on the Gaussian curvature, which can dynamically adjust the local resolution. Geometric information can be captured more effectively, improving the accuracy of surface reconstruction. Finally, implicit functions are adopted to perform global fitting, and the zero-level set is obtained through the octree isosurface extraction algorithm. In solving the iterative linear system, multi-thread techniques are implemented for parallel computation to enhance the execution performance of the algorithm. Experimental results demonstrate that the curvature-adaptive octree based on Gaussian curvature, can effectively capture complex geometric details, and the algorithm accomplishes high-precision surface reconstruction at different scales. Furthermore, multi-thread technology enhances local and global computing performance, ensuring the algorithm's effectiveness in processing large-scale data.