also compared in Figure \[fig:pr\] which intuitively shows the performances of these three methods.

Unsupervised Upsampling for Point Clouds
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Benefit from the design of local to global reconstruction, it is competent for our L2G-AE to be applied in the unsupervised point cloud upsampling application. In the local reconstruction, a dense point cloud is obtained by reconstructing multiple local scales with overlapping. Therefore, it is convenient to produce the upsampling results by downsampling from the dense local reconstructed results using some unsupervised methods, such as random sampling or farthest point sampling. As far as we know, L2G-AE is the first method which performs point cloud upsampling with deep neural networks in an unsupervised manner. To evaluate the performance of L2G-AE, We compare our method on relatively sparse (625 points) inputs with state-of-the-art supervised point cloud upsampling methods, including PU-Net [@yu2018pu] and EC-Net [@yu2018ecnet]. The target of upsampling is to generate a dense point clouds with 10000 points. For PU-Net and EC-Net, the $16 \times$ results (10000 points) are obtained from inputs (625 points) in a supervised manner. Differently, L2G-AE first obtains the local reconstruction results and then downsamples them to 10000 points.

![Some upsampled results of L2G-AE.[]{data-label="fig:upsampled"}](upsampling.pdf){width="8cm" height="6cm"}

As shown 