Deep Magnification-Arbitrary Upsampling over 3D Point Clouds

by   Yue Qian, et al.

This paper addresses the problem of generating dense point clouds from given sparse point clouds to model the underlying geometric structures of objects/scenes. To tackle this challenging issue, we propose a novel end-to-end learning based framework, namely MAPU-Net. Specifically, by taking advantage of the linear approximation theorem, we first formulate the problem explicitly, which boils down to determining the interpolation weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted interpolation weights and normal-guided displacements, by analyzing the local geometry of the input point cloud. MAPU-Net can be interpreted by the explicit formulation, and thus is more memory-efficient than existing ones. In sharp contrast to the existing methods that work only for a pre-defined and fixed upsampling factor, MAPU-Net, a single neural network with one-time training, can handle an arbitrary upsampling factor, which is highly desired in real-world applications. In addition, we propose a simple yet effective training strategy to drive such a flexible ability. Extensive experiments on both synthetic and real world data demonstrate the superiority of the proposed MAPU-Net over state-of-the-art methods both quantitatively and qualitatively. To the best of our knowledge, this is the first end-to-end learning based method that is capable of achieving magnification-arbitrary upsampling over 3D point clouds.


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