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- W4386481880 abstract "Abstract To address the limitations on inadequate completion of shape details for traditional methods, a novel coarse-to-fine point completion network (DCSE-PCN) is introduced in this paper through the modules of local details compensation and shape structure enhancement for effective geometric learning. The coarse completion stage of our network consists of two branches — a shape structure recovery branch and a local details compensation branch, which can recover the overall shape of the underlying model and the shape details of incomplete point cloud through feature learning and hierarchical feature fusion. The fine completion stage of our network employs the structure enhancement module to reinforce the correlated shape structures of the coarse repaired shape (such as regular arrangement or symmetry), thus obtaining the completed geometric shape with finer-grained details. Extensive experimental results on the ShapeNet dataset and ModelNet dataset validate the effectiveness of our DCSE-PCN, which can recover the shape details of the underlying point cloud while maintaining its overall shape. Compared to the existing methods, our presented coarse-to-fine completion network is shown its competitive performance on both CD and EMD distance error and is also robust to different degrees of data incompleteness and model noise." @default.
- W4386481880 created "2023-09-07" @default.
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- W4386481880 date "2023-09-06" @default.
- W4386481880 modified "2023-09-27" @default.
- W4386481880 title "DCSE-PCN: A Coarse-to-Fine Point Completion Network with Details Compensation and Structure Enhancement" @default.
- W4386481880 doi "https://doi.org/10.21203/rs.3.rs-3318728/v1" @default.
- W4386481880 hasPublicationYear "2023" @default.
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