Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3 novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs) to better encode local point information . Our upsampling modules are versatile and can be incorporated into any point cloud upsampling pipeline. We show how our 3 modules consistently improve state-of-the-art methods in all point upsampling metrics . We also propose a new multi-scale point feature extractor, called Inception DenseGCN. We modify current Inception GCN algorithms by introducing DenseGCN blocks. By aggregating data at multiple scales, our new feature extractor is more resilient to density changes along point cloud surfaces . We combine Inception DenseGCN with one of our upsampling modules (NodeShuffle) into a new point upsampling pipeline: PU-GCN. We show both qualitatively and quantitatively the  advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity . The website and source code of this work is available at URL and URL respectively.
The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. We propose three novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs) to better encode local point information from the point neighborhood. These upsampling modules are versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how these modules consistently improve state-of-the-art upsampling methods . We also propose a new multi-scale point feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, this feature extractor enables further performance gain in the final upsampled point clouds . We combine Inception DenseGCN with one of our upsampling modules (NodeShuffle) into a new point upsampling pipeline: PU-GCN. We show qualitatively and quantitatively the significant advantages of PU-GCN over the state-of-the-art . The website and source code of this work are available at URL and URL respectively.
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Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3
The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. We propose three
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from the point neighborhood. These
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We show how our 3
Extensive experiments show how these
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methods in all point upsampling metrics
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We modify current Inception GCN algorithms by introducing DenseGCN blocks. By aggregating data
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our new feature extractor is more resilient to density changes along point cloud surfaces
this feature extractor enables further performance gain in the final upsampled point clouds
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in terms of fine-grained upsampling quality and point cloud uniformity
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