Performance analysis of different surface reconstruction algorithms for 3D reconstruction of outdoor objects from their digital images.
Performance analysis of different surface reconstruction algorithms for 3D reconstruction of outdoor objects from their digital images.
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DOI：
10.1186/s40064-016-2425-9
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Chakravarty D
中科院分区：
文献类型：
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作者：
Maiti A;Chakravarty D
从地质体的数字图像中进行三维重建是一种省时、方便的研究被建模地质体结构特征的方法。本文提出了一种三维重建方法，它可以用来生成逼真的三维防水表面的不同不规则形状的物体，从数字图像序列的对象。这里描述的3D重建方法是鲁棒的、简单的，并且可以容易地用于从其数字图像序列重建任何对象的防水3D表面。在这里，不同对象的数字图像用于构建稀疏的，然后是对象的密集3D点云。这些图像获得的点云，然后用于生成逼真的3D表面，使用不同的表面重建算法，如泊松重建和球旋转算法。这些算法的不同控制参数，确定，影响重建的三维表面的质量和计算时间。研究了这些控制参数对由不同密度的点云生成三维曲面的影响。结果表明，泊松重建的表面质量取决于每节点采样数（SN）显着，更大的SN值导致更好的质量表面。此外，使用球旋转算法生成的3D表面的质量被发现是高度依赖于聚类半径和角度阈值。从这项研究中获得的结果给读者的文章一个有价值的洞察不同的控制参数的影响，确定重建的表面质量。本文的在线版本（doi：10.1186/s40064-016-2425-9）包含补充材料，可供授权用户使用。
3D reconstruction of geo-objects from their digital images is a time-efficient and convenient way of studying the structural features of the object being modelled. This paper presents a 3D reconstruction methodology which can be used to generate photo-realistic 3D watertight surface of different irregular shaped objects, from digital image sequences of the objects. The 3D reconstruction approach described here is robust, simplistic and can be readily used in reconstructing watertight 3D surface of any object from its digital image sequence. Here, digital images of different objects are used to build sparse, followed by dense 3D point clouds of the objects. These image-obtained point clouds are then used for generation of photo-realistic 3D surfaces, using different surface reconstruction algorithms such as Poisson reconstruction and Ball-pivoting algorithm. Different control parameters of these algorithms are identified, which affect the quality and computation time of the reconstructed 3D surface. The effects of these control parameters in generation of 3D surface from point clouds of different density are studied. It is shown that the reconstructed surface quality of Poisson reconstruction depends on Samples per node (SN) significantly, greater SN values resulting in better quality surfaces. Also, the quality of the 3D surface generated using Ball-Pivoting algorithm is found to be highly depend upon Clustering radius and Angle threshold values. The results obtained from this study give the readers of the article a valuable insight into the effects of different control parameters on determining the reconstructed surface quality. The online version of this article (doi:10.1186/s40064-016-2425-9) contains supplementary material, which is available to authorized users.