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Exploiting color for graph-based 3D point cloud denoising
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2021-01-21 , DOI: 10.1016/j.jvcir.2021.103027
Muhammad Abeer Irfan , Enrico Magli

A point cloud is a representation of a 3D scene as a discrete collection of geometry plus other attributes such as color, normal, transparency associated with each point. The traditional acquisition process of a 3D point cloud, e.g. using depth information acquired directly by active sensors or indirectly from multi-viewpoint images, suffers from a significant amount of noise. Hence, the problem of point cloud denoising has recently received a lot of attention. However, most existing techniques attempt to denoise only the geometry of each point, based on the geometry information of the neighboring points; there are very few works at all considering the problem of denoising the color attributes of a point cloud. In this paper, we move beyond the state of the art and we propose a novel technique employing graph-based optimization, taking advantage of the correlation between geometry and color, and using it as a powerful tool for several different tasks, i.e. color denoising, geometry denoising, and combined geometry and color denoising. The proposed method is based on the notion that the correct location of a point also depends on the color attribute and not only the geometry of the neighboring points, and the correct color also depends on the geometry of the neighbors. The proposed method constructs a suitable k-NN graph from geometry and color and applies graph-based convex optimization to obtain the denoised point cloud. Extensive simulation results on both real-world and synthetic point clouds show that the proposed denoising technique outperforms state-of-the-art methods using both subjective and objective quality metrics.



中文翻译:

利用颜色进行基于图的3D点云去噪

点云是3D场景的表示,表示为几何的离散集合以及其他属性,例如与每个点关联的颜色,法线,透明度。传统的3D点云采集过程(例如,使用由主动传感器直接获取的深度信息或间接从多视点图像获取的深度信息)会遭受大量噪声的困扰。因此,点云去噪的问题近来受到了很多关注。然而,大多数现有技术试图基于相邻点的几何信息来仅对每个点的几何进行消噪。几乎没有什么作品考虑了对点云的颜色属性进行去噪的问题。在本文中,我们超越了现有技术,提出了一种采用基于图的优化的新技术,几何和颜色之间的相关性,并将其用作强大的工具来执行多个不同的任务,即颜色去噪,几何去噪以及组合的几何和颜色去噪。所提出的方法基于这样的概念,即点的正确位置还取决于颜色属性,不仅取决于相邻点的几何形状,而且正确的颜色还取决于邻居的几何形状。所提出的方法从几何和颜色构造合适的k -NN图,并应用基于图的凸优化来获得去噪点云。在现实世界和合成点云上的大量仿真结果表明,所提出的降噪技术优于使用主观和客观质量指标的最新技术。

更新日期:2021-01-28
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