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• W3155369577 abstract "We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net. Specifically, the Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, which can capture detailed shape prior. Then we design an iterative refinement unit to generate complete shapes with fine-grained details by integrating prior information. Experiments are conducted on the PCN dataset and the Completion3D benchmark, demonstrating the state-of-the-art performance of the proposed ASFM-Net. Our method achieves the 1st place in the leaderboard of Completion3D and outperforms existing methods with a large margin, about 12%. The codes and trained models are released publicly at https://github.com/Yan-Xia/ASFM-Net." @default.
• W3155369577 created "2021-04-26" @default.
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• W3155369577 date "2021-04-19" @default.
• W3155369577 modified "2023-09-27" @default.
• W3155369577 title "ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion" @default.
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• W3155369577 doi "https://doi.org/10.48550/arxiv.2104.09587" @default.
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