School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Sensors (Basel). 2020 Jan 18;20(2):534. doi: 10.3390/s20020534.
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.
截断符号距离场 (TSDF) 已被应用于基于手持式激光线扫描仪的工业产品三维重建中的快速、准确和灵活的几何融合方法。然而，这种方法在薄产品的表面重建中存在一些问题。表面网格会塌陷到模型内部，导致一些拓扑错误，例如重叠、交叉或间隙。同时，现有的 TSDF 方法通过大量使用图形处理单元 (GPU) 内存来确保实时性能，这限制了重建场景的规模。在这项工作中，我们对现有的 TSDF 方法提出了三个改进，包括：（i）实时处理中的薄表面属性判断方法，解决了薄表面相对两侧干扰的问题；我们通过表面法向量与视线观察线之间的夹角来区分来自薄表面不同部分的测量值；（ii）一种后处理方法，用于自动检测和修复可能发生薄表面属性误判的某些区域的拓扑错误；（iii）一种集成中央处理器 (CPU) 和 GPU 资源的框架，用于实现我们的 3D 重建方法，该方法确保实时性能并减少 GPU 内存使用。提出的结果表明，该方法可以提供更准确的薄表面 3D 重建，与具有 0.02 毫米精度的最先进的激光线扫描仪相似。在性能方面，该算法可以保证 GPU 内存占用低于 500MB 时帧率超过 60 帧/秒 (FPS)。总的来说，该方法可以实现薄表面的实时高精度 3D 重建。
