RFNet: Recurrent Forward Network for Dense Point Cloud Completion

Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, Yong Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12508-12517


Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning-based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we propose a novel Recurrent Forward Network (RFNet), which is composed of three modules: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline. The RSP introduces details from the original incomplete models to refine the completion results. Besides, we propose a Sampling Chamfer Distance to better capture the shapes of models and a new Balanced Expansion Constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve the state-of-the-art with lower memory cost and faster convergence.

Related Material

@InProceedings{Huang_2021_ICCV, author = {Huang, Tianxin and Zou, Hao and Cui, Jinhao and Yang, Xuemeng and Wang, Mengmeng and Zhao, Xiangrui and Zhang, Jiangning and Yuan, Yi and Xu, Yifan and Liu, Yong}, title = {RFNet: Recurrent Forward Network for Dense Point Cloud Completion}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {12508-12517} }