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MVPCC-Net: Multi-View Based Point Cloud Completion Network for MLS data
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DOI：10.1016/j.imavis.2023.104675.png)
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摘要
En 中文
In this paper, we introduce a novel multi view-based method for completing high-resolution 3D point clouds of partial object shapes obtained by mobile laser scanning (MLS) platforms. Our approach estimates both the geom-etry and color cues of the missing or incomplete object segments, by projecting the 3D input point cloud by mul-tiple virtual cameras, and performing 2D inpainting in the image domains of the different views. In contrast to existing state-of-the-art methods, our method can generate point clouds consisting of a variable number of points, depending on the detailedness of the input measurement, which property highly facilitates the efficient processing of MLS data with inhomogeneous point density. For training and quantitative evaluation of the pro-posed method, we provide a new point cloud dataset that consists of both synthetic point clouds of four different street objects with accurate ground truth, and real MLS measurements of partially or fully scanned vehicles. The quantitative and qualitative experiments on the provided dataset demonstrate that our method surpasses state-of-the-art approaches in reconstructing the local fine geometric structures as well as in estimating the overall shape and color pattern of the objects.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Multi-view
3D point cloud completion
Deep learning
Mobile laser scanning
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