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AEDNet: Adaptive Embedding and Multiview-Aware Disentanglement for Point Cloud Completion

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Point cloud completion involves inferring missing parts of 3D objects from incomplete point cloud data. It requires a model that understands the global structure of the object and reconstructs local details. To this end, we propose a global perception and local attention network, termed AEDNet, for point cloud completion. The proposed AEDNet utilizes designed adaptive point cloud embedding and disentanglement (AED) module in both the encoder and decoder to globally embed and locally disentangle the given point cloud. In the AED module, we introduce a global embedding operator that employs the devised slot attention to compose point clouds into different embeddings, each focusing on specific parts of 3D objects. Then, we proposed a multiview-aware disentanglement operator to disentangle geometric information from those embeddings in the 3D viewpoints generated on a unit sphere. These 3D viewpoints enable us to observe point clouds from the outside rather than from within, resulting in a comprehensive understanding of their geometry. Additionally, the arbitrary number of points and point-wise features can be disentangled by changing the number of viewpoints, reaching high flexibility. Experiments show that our proposed method achieves state-of-the-art results on both MVP and PCN datasets.

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Acknowledgements

This research was financially supported by the Australian Research Council (ARC DP210101682, DP210102674, DP220102197), UWA Research Collaboration Award (2023/GR001286) and received additional partial funding from the National Natural Science Foundation of China (No. U20A20185, 62372491), the Guangdong Basic and Applied Basic Research Foundation (2022B1515020103, 2023B1515120087), the Shenzhen Science and Technology Program (No. RCYX20200714114641140).

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Fu, Z. et al. (2025). AEDNet: Adaptive Embedding and Multiview-Aware Disentanglement for Point Cloud Completion. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15069. Springer, Cham. https://doi.org/10.1007/978-3-031-73247-8_8

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