Abstract

The truncated signed distance field (TSDF) has been applied as a fast, accurate, and flexible geometric fusion method in 3D reconstruction of industrial products based on a hand-held laser line scanner. However, this method has some problems for the surface reconstruction of thin products. The surface mesh will collapse to the interior of the model, resulting in some topological errors, such as overlap, intersections, or gaps. Meanwhile, the existing TSDF method ensures real-time performance through significant graphics processing unit (GPU) memory usage, which limits the scale of reconstruction scene. In this work, we propose three improvements to the existing TSDF methods, including: (i) a thin surface attribution judgment method in real-time processing that solves the problem of interference between the opposite sides of the thin surface; we distinguish measurements originating from different parts of a thin surface by the angle between the surface normal and the observation line of sight; (ii) a post-processing method to automatically detect and repair the topological errors in some areas where misjudgment of thin-surface attribution may occur; (iii) a framework that integrates the central processing unit (CPU) and GPU resources to implement our 3D reconstruction approach, which ensures real-time performance and reduces GPU memory usage. The proposed results show that this method can provide more accurate 3D reconstruction of a thin surface, which is similar to the state-of-the-art laser line scanners with 0.02 mm accuracy. In terms of performance, the algorithm can guarantee a frame rate of more than 60 frames per second (FPS) with the GPU memory footprint under 500 MB. In total, the proposed method can achieve a real-time and high-precision 3D reconstruction of a thin surface.

Highlights

  • Recent years have witnessed the release of a large number of fast, accurate depth sensors

  • The accuracy of the proposed method is evaluated by comparing it with two existing hand-held 3D laser line scanners: Creaform HandySCAN700 [7] and Scantech HSCAN771 [8]

  • The apparent improvements in the figures are marked with red ellipses

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Summary

Introduction

Recent years have witnessed the release of a large number of fast, accurate depth sensors. With the opening up of graphics processors to general computing, 3D reconstruction by the depth sensors has attracted new attention in the field of computer vision [1] It has a wide range of applications in reverse engineering, 3D printing, industrial inspection, robotics, augmented reality (AR), autonomous driving, and other fields [2,3,4]. Traditional depth sensors, such as the Structured Light Camera, Time of Flight (ToF) Camera, and Terrestrial Laser Scanner (TLS), provide depth data as depth images or sets of unorganized points. Given its advantages in speed, accuracy, and flexibility [6], this kind of scanner, such as HandySCAN [7], HSCAN [8], and FreeScan [9] have been widely used in high-precision 3D measurement applications in industrial manufacturing

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