3D Reconstruction of Unstructured Objects Using Information From Multiple Sensors

点云 由运动产生的结构 计算机科学 人工智能 计算机视觉 曲面重建 三维重建 分割 迭代重建 噪音(视频) 特征(语言学) 算法 重建算法 比例因子(宇宙学) 点(几何) 运动估计 曲面(拓扑) 数学 图像(数学) 几何学 语言学 哲学 物理 宇宙学 量子力学 空间的度量展开 暗能量
作者
Hui Chen,Fangyong Xu,Wanquan Liu,Dongge Sun,Peter Liu,Muhammad Ilyas Menhas,Bilal Ahmad
出处
期刊:IEEE Sensors Journal [IEEE Sensors Council]
卷期号:21 (23): 26951-26963 被引量:8
标识
DOI:10.1109/jsen.2021.3121343
摘要

The Structure-from-Motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise, which could degrade the estimation of reconstructed objects especially for smooth surfaces with few features. The other drawback is the accuracy and speed of SfM based methods depend on the uncertain number of images. To overcome these limitations, this paper proposes a novel 3D reconstruction method for unstructured objects based on the structure from motion in combination with the structured light, in which the point sets of structured light and the point sets of structure from motion can come from different target objects. Since the two point sets coming from multiple sensors do not scale well for register, making it difficult to find corresponding points, a scaled principal component analysis algorithm is proposed for the registration to overcome the impact due to large scale variance. With a large scale factor, a recalculated registration center is proposed via feature region segmentation to achieve point cloud registration again. The two point sets are matched using the proposed optimization method to complete 3D reconstruction. Surface reconstruction is performed using the Poisson algorithm to obtain a smooth surface. The proposed method is tested on some simple structured objects and real-life data of complex unstructured objects collected using range sensors. Compared with several state-of-the-art algorithms, experimental results confirm its potential for surface reconstruction from depth data calculated from the two sets.
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