Reconstructing and processing the 3D objects are popular activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on the methods like geometric modeling, a branch of applied mathematics and computational geometry, or the machine learning algorithms based on image processing. The computation of geometrical objects includes processing the curves and surfaces, subdivision, simplification, meshing, holes filling, reconstructing, and refining the 3D surface objects on both point cloud data and triangular mesh. While the machine learning methods are developed using deep learning models. With the support of 3D laser scan devices and Lidar techniques, the obtained dataset is close to original shape of the real objects. Besides, the photography and its application based on the modern techniques in recent years help us collect data and process the 3D models more precise. This article proposes an improved method for filling holes on the 3D object surface based on an automatic segmentation. Instead of filling the hole directly as the existing methods, we now subdivide the hole before filling it. The hole is first determined and segmented automatically based on computation of its local curvature. It is then filled on each part of the hole to match its local curvature shape. The method can work on both 3D point cloud surfaces and triangular mesh surface. Comparing to the state of the art methods, our proposed method obtained higher accuracy of the reconstructed 3D objects.

翻译：三维物体的重建与处理是计算机图形学、图像处理和计算机视觉研究领域的热门工作。三维物体的处理基于多种方法，例如作为应用数学与计算几何分支的几何建模，或基于图像处理的机器学习算法。几何物体的计算包括处理点云数据和三角网格上的曲线与曲面、细分、简化、网格化、孔洞填充、重建以及三维曲面物体的精化。而机器学习方法则利用深度学习模型进行开发。在三维激光扫描设备与激光雷达技术的支持下，获取的数据集更接近真实物体的原始形态。此外，近年来基于现代技术的摄影及其应用帮助我们更精确地收集数据和三维模型。本文提出一种基于自动分割的三维物体表面孔洞填充改进方法。与现有方法直接填充孔洞不同，我们先将孔洞进行细分后再填充。首先根据局部曲率计算自动确定并分割孔洞，然后对孔洞的各部分分别填充以匹配其局部曲率形态。该方法可同时适用于三维点云曲面和三角网格曲面。与现有先进方法相比，我们的方法获得了更高的三维物体重建精度。