A 3D Surface Reconstruction Method for Large-Scale Point Cloud Data

Abstract

Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

“…New low-cost data acquisition devices are emerging to digitize 3D objects with enhanced quality and reduced acquisition times. The 3D surface reconstruction of point cloud data has been a critical problem in the field of computer graphics and vision [2], and many scholars have done extensive research on it [10]. However, in most applications, surface reconstruction techniques end up generating dead or frozen models without any information of the constituting features and which cannot be used for product development processes especially when it comes to late design changes.…”