A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing

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.

Towards Open-Source Web-Based 3D Reconstruction for Non-Professionals

“…In addition, they introduce a web-based interface to edit the resulting 3D models (cropping for removing unwanted parts of the reconstruction, and reorienting) but do not evaluate it in a user study Bouck-Standen et al (2018). Sheng et al introduce a set of algorithms focusing on creating compact surface reconstructions suitable for 3D printing through a computationally efficient process Sheng et al (2018). The data is stored in a cloud-based server and the point cloud data can be viewed through an webbased interface.…”

Shape Reconstruction from Point Clouds Using Closed Form Solution of a Fourth-Order Partial Differential Equation

“…Rodrigues et al obtained an analytical solution of a Laplace equation and used it in 3D data compression and reconstruction [21]. Sheng et al integrated a point cloud update algorithm, a rapid iterative closest point algorithm, and an improved Poisson surface reconstruction algorithm together to improve the efficiency of surface reconstruction [22].…”

3D Reconstruction and Texture Refinement with Image Enhancement for Improved Visual Quality

“…Wang et al, 2018) For 3D reconstruction, some methods generate the surface from a point cloud constructed from RGB-D images. This reconstruction can be without texture mapping (F. Wang & Hauser, 2019), (Sheng et al, 2018), (Tzionas & Gall, 2015), (Gao et al, 2019), (K. Wang et al, 2014), (Zhong et al, 2019), (Mi et al, 2020), and(Kazhdan et al, 2013) or with texture mapping (Vrubel et al, 2009) and (Tucci et al, 2012). Some methods use a priori templates (Hao et al, 2019).…”