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- W2890896661 abstract "As 3D scanning devices and depth sensors advance, point clouds have attracted increasing attention as a format for 3D object representation, with applications in various fields such as tele-presence, navigation and heritage reconstruction. However, point clouds usually exhibit holes of missing data, mainly due to the limitation of acquisition techniques and complicated structure. Hence, we propose an efficient point cloud inpainting method, leveraging on graph signal processing and based on the observation of non-local self-similarity in point clouds. Specifically, we split a point cloud into fixed-size cubes as the processing unit, and globally search for the most similar cube to the target cube with holes inside. The similarity metric between two cubes is defined based on the direct component and the proposed anisotropic graph total variation of normals in each cube. We then formulate the hole-filling step as an optimization problem, based on the selected most similar cube and regularized by a graph-signal smoothness prior. Experimental results show that the proposed approach outperforms three competing methods significantly, both in objective and subjective quality." @default.
- W2890896661 created "2018-09-27" @default.
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- W2890896661 date "2018-10-01" @default.
- W2890896661 modified "2023-10-13" @default.
- W2890896661 title "Point Cloud Inpainting on Graphs from Non-Local Self-Similarity" @default.
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- W2890896661 doi "https://doi.org/10.1109/icip.2018.8451550" @default.
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