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Point normal, as an intrinsic geometric property of 3D objects, not onlyserves conventional geometric tasks such as surface consolidation andreconstruction, but also facilitates cutting-edge learning-based techniques forshape analysis and generation. In this paper, we propose a normal refinementnetwork, called Refine-Net, to predict accurate normals for noisy point clouds.Traditional normal estimation wisdom heavily depends on priors such as surfaceshapes or noise distributions, while learning-based solutions settle for singletypes of hand-crafted features. Differently, our network is designed to refinethe initial normal of each point by extracting additional information frommultiple feature representations. To this end, several feature modules aredeveloped and incorporated into Refine-Net by a novel connection module.Besides the overall network architecture of Refine-Net, we propose a newmulti-scale fitting patch selection scheme for the initial normal estimation,by absorbing geometry domain knowledge. Also, Refine-Net is a generic normalestimation framework: 1) point normals obtained from other methods can befurther refined, and 2) any feature module related to the surface geometricstructures can be potentially integrated into the framework. Qualitative andquantitative evaluations demonstrate the clear superiority of Refine-Net overthe state-of-the-arts on both synthetic and real-scanned datasets. Our code isavailable at https://github.com/hrzhou2/refinenet.

Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds