Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details

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# PU-Net: Point Cloud Upsampling Network

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.

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# PU-GAN: a Point Cloud Upsampling Adversarial Network

Point clouds acquired from range scans are often sparse, noisy, and non-uniform.

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# Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling.

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# PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

30 Nov 2019guochengqian/PU-GCN

We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.

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# PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling

Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.

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# 007: Democratically Finding The Cause of Packet Drops

20 Feb 2018behnazak/Vigil-007SourceCode

Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.

Ranked #1 on Chinese Named Entity Recognition on MSRA (0..5sec metric)

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# Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud

Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.

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