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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References
Chen, Z., et al.: Anchorformer: point cloud completion from discriminative nodes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13581–13590 (2023)
Chen, Z., et al.: Learning 3d shape latent for point cloud completion. IEEE Trans. Multimedia (2024)
Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628–644. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_38
Dai, A., Ruizhongtai Qi, C., Nießner, M.: Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5868–5877 (2017)
Eslami, S., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Hinton, G.E., et al.: Attend, infer, repeat: fast scene understanding with generative models. Adv. Neural Inf. Process. Syst. 29 (2016)
Fu, Z., et al.: Vapcnet: viewpoint-aware 3d point cloud completion. In: IEEE International Conference on Computer Vision, pp. 12108–12118 (2023)
Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: Learning a predictable and generative vector representation for objects. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 484–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_29
Greff, K., et al.: Multi-object representation learning with iterative variational inference. In: International Conference on Machine Learning, pp. 2424–2433. PMLR (2019)
Greff, K., Van Steenkiste, S., Schmidhuber, J.: Neural expectation maximization. Adv. Neural Inf. Process. Syst. 30 (2017)
Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A papier-mâché approach to learning 3d surface generation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 216–224 (2018)
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4338–4364 (2020)
Han, X., Li, Z., Huang, H., Kalogerakis, E., Yu, Y.: High-resolution shape completion using deep neural networks for global structure and local geometry inference. In: IEEE International Conference on Computer Vision, pp. 85–93 (2017)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)
Huang, Z., Yu, Y., Xu, J., Ni, F., Le, X.: Pf-net: point fractal network for 3d point cloud completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7662–7670 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kosiorek, A., Kim, H., Teh, Y.W., Posner, I.: Sequential attend, infer, repeat: Generative modelling of moving objects. Adv. Neural Inf. Process. Syst. 31 (2018)
Li, J., Guo, S., Wang, L., Han, S.: Completedt: point cloud completion with information-perception transformers. Neurocomputing 592, 127790 (2024)
Liu, M., Sheng, L., Yang, S., Shao, J., Hu, S.M.: Morphing and sampling network for dense point cloud completion. In: AAAI Conference on Artificial Intelligence, vol. 34, pp. 11596–11603 (2020)
Locatello, F., et al.: Object-centric learning with slot attention. Adv. Neural. Inf. Process. Syst. 33, 11525–11538 (2020)
Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2837–2845 (2021)
Lyu, Z., Kong, Z., Xu, X., Pan, L., Lin, D.: A conditional point diffusion-refinement paradigm for 3d point cloud completion. arXiv preprint arXiv:2112.03530 (2021)
Nie, Y., et al.: Skeleton-bridged point completion: from global inference to local adjustment. Adv. Neural. Inf. Process. Syst. 33, 16119–16130 (2020)
Pan, L.: Ecg: edge-aware point cloud completion with graph convolution. IEEE Rob. Autom. Lett. 5(3), 4392–4398 (2020)
Pan, L., et al.: Variational relational point completion network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8524–8533 (2021)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)
Shao, J.: Mathematical Statistics. Springer, Heidelberg (2003)
Su, H., et al.: Splatnet: sparse lattice networks for point cloud processing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2530–2539 (2018)
Tchapmi, L.P., Kosaraju, V., Rezatofighi, H., Reid, I., Savarese, S.: Topnet: structural point cloud decoder. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 383–392 (2019)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Viswanath, D.: Random fibonacci sequences and the number 1.13198824…. Math. Comput. 69(231), 1131–1155 (2000)
Wang, J., Cui, Y., Guo, D., Li, J., Liu, Q., Shen, C.: Pointattn: you only need attention for point cloud completion. arXiv preprint arXiv:2203.08485 (2022)
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-cnn: octree-based convolutional neural networks for 3d shape analysis. ACM Trans. Graph. (TOG) 36(4), 1–11 (2017)
Wang, X., Ang Jr, M.H., Lee, G.H.: Cascaded refinement network for point cloud completion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 790–799 (2020)
Wang, Y., Tan, D.J., Navab, N., Tombari, F.: Learning local displacements for point cloud completion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1568–1577 (2022)
Wang, Y., Wang, L., Hu, Q., Liu, Y., Zhang, Y., Guo, Y.: Panoptic segmentation of 3d point clouds with gaussian mixture model in outdoor scenes. Visual Intell. 2(1), 10 (2024)
Wen, X., Han, Z., Cao, Y.P., Wan, P., Zheng, W., Liu, Y.S.: Cycle4completion: unpaired point cloud completion using cycle transformation with missing region coding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 13080–13089 (2021)
Wen, X., et al.: Pmp-net: point cloud completion by learning multi-step point moving paths. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7443–7452 (2021)
Wen, X.: Pmp-net++: point cloud completion by transformer-enhanced multi-step point moving paths. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 852–867 (2022)
Wu, Z., et al.: 3d shapenets: a deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)
Xiang, P., et al.: Snowflakenet: point cloud completion by snowflake point deconvolution with skip-transformer. In: IEEE International Conference on Computer Vision, pp. 5499–5509 (2021)
Xie, C., Wang, C., Zhang, B., Yang, H., Chen, D., Wen, F.: Style-based point generator with adversarial rendering for point cloud completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4619–4628 (2021)
Xie, H., Yao, H., Zhou, S., Mao, J., Zhang, S., Sun, W.: GRNet: gridding residual network for dense point cloud completion. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 365–381. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_21
Yan, X., Zheng, C., Li, Z., Wang, S., Cui, S.: Pointasnl: robust point clouds processing using nonlocal neural networks with adaptive sampling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5589–5598 (2020)
Yang, Y., Feng, C., Shen, Y., Tian, D.: Foldingnet: point cloud auto-encoder via deep grid deformation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 206–215 (2018)
Yu, L., Li, X., Fu, C.W., Cohen-Or, D., Heng, P.A.: Pu-net: point cloud upsampling network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790–2799 (2018)
Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., Zhou, J.: Pointr: diverse point cloud completion with geometry-aware transformers. In: IEEE International Conference on Computer Vision, pp. 12498–12507 (2021)
Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: Pcn: point completion network. In: International Conference on 3D Vision (3DV), pp. 728–737. IEEE (2018)
Zhang, W., Yan, Q., Xiao, C.: Detail preserved point cloud completion via separated feature aggregation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 512–528. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_31
Zhang, Y., Wang, L., Li, K., Fu, Z., Guo, Y.: Slfnet: a stereo and lidar fusion network for depth completion. IEEE Rob. Autom. Lett. 7(4), 10605–10612 (2022)
Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: IEEE International Conference on Computer Vision, pp. 16259–16268 (2021)
Zhou, H., et al.: Seedformer: patch seeds based point cloud completion with upsample transformer. In: European Conference on Computer Vision, pp. 416–432. Springer, Heidelberg (2022). DOI: https://doi.org/10.1007/978-3-031-20062-5_24
Zhou, L., Du, Y., Wu, J.: 3d shape generation and completion through point-voxel diffusion. In: IEEE International Conference on Computer Vision, pp. 5826–5835 (2021)
Zhu, Z., Chen, H., He, X., Wang, W., Qin, J., Wei, M.: Svdformer: complementing point cloud via self-view augmentation and self-structure dual-generator. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14508–14518 (2023)
Zong, D., Sun, S., Zhao, J.: Ashf-net: adaptive sampling and hierarchical folding network for robust point cloud completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3625–3632 (2021)
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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