Abstract
The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.
Highlights
To reduce the complexity of the algorithm and generate a lightweight 3D model, in this paper, an online lightweight surface reconstruction algorithm is proposed, which is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR)
To repair the mesh holes that can be generated by the Poisson surface reconstruction algorithm, an iterative postprocessing algorithm for PDE patch generation based on biharmoniclike fourth-order PDEs is executed successively in IPSR, which reduces the amount of reconstructed mesh data
This reconstruction ensures that a reduced data volume of the 3D model meets the requirement for web-based data transmission; (2) a dynamic visualization framework for point cloud data based on WebSocket, which achieves online dynamic visualization of point cloud data in high concurrency environments; (3) an online personalized customization system oriented toward 3D printing, which dynamically visualizes the point cloud data through the 3D scanning process and the efficient and rapid reconstruction of the lightweight mesh in the web environment
Summary
To reduce the complexity of the algorithm and generate a lightweight 3D model, in this paper, an online lightweight surface reconstruction algorithm is proposed, which is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The novel contributions in this paper are as follows: (1) an online lightweight surface reconstruction algorithm, in which the lightweight operations are conducted at every step, from point cloud data acquisition to preprocessing and to surface reconstruction This reconstruction ensures that a reduced data volume of the 3D model meets the requirement for web-based data transmission; (2) a dynamic visualization framework for point cloud data based on WebSocket, which achieves online dynamic visualization of point cloud data in high concurrency environments; (3) an online personalized customization system oriented toward 3D printing, which dynamically visualizes the point cloud data through the 3D scanning process and the efficient and rapid reconstruction of the lightweight mesh in the web environment.
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