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Most existing point cloud upsampling methods have roughly three steps:feature extraction, feature expansion and 3D coordinate prediction.However,they usually suffer from two critical issues: (1)fixed upsampling rateafter one-time training, since the feature expansion unit is customized foreach upsampling rate; (2)outliers or shrinkage artifact caused by thedifficulty of precisely predicting 3D coordinates or residuals of upsampledpoints. To adress them, we propose a new framework for accurate point cloudupsampling that supports arbitrary upsampling rates. Our method firstinterpolates the low-res point cloud according to a given upsampling rate. Andthen refine the positions of the interpolated points with an iterativeoptimization process, guided by a trained model estimating the differencebetween the current point cloud and the high-res target. Extensive quantitativeand qualitative results on benchmarks and downstream tasks demonstrate that ourmethod achieves the state-of-the-art accuracy and efficiency.

Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions