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In this paper, we propose a Point Fractal Network (PF-Net), a novellearning-based approach for precise and high-fidelity point cloud completion.Unlike existing point cloud completion networks, which generate the overallshape of the point cloud from the incomplete point cloud and always changeexisting points and encounter noise and geometrical loss, PF-Net preserves thespatial arrangements of the incomplete point cloud and can figure out thedetailed geometrical structure of the missing region(s) in the prediction. Tosucceed at this task, PF-Net estimates the missing point cloud hierarchicallyby utilizing a feature-points-based multi-scale generating network. Further, weadd up multi-stage completion loss and adversarial loss to generate morerealistic missing region(s). The adversarial loss can better tackle multiplemodes in the prediction. Our experiments demonstrate the effectiveness of ourmethod for several challenging point cloud completion tasks.

PF-Net: Point Fractal Network for 3D Point Cloud Completion