A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling
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Abstract (arXiv)
Recently, deep learning methods have shown great success in 3D point cloud upsampling. Among these methods, many feature expansion units were proposed to complete point expansion at the end. In this paper, we compare various feature expansion units by both theoretical analysis and quantitative experiments. We show that most of the existing feature expansion units process each point feature independently, while ignoring the feature interaction among different points. Further, inspired by upsampling module of image super-resolution and recent success of dynamic graph CNN on point clouds, we propose a novel feature expansion units named ProEdgeShuffle. Experiments show that our proposed method can achieve considerable improvement over previous feature expansion units.
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References · 12
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2018Self-Attention Generative Adversarial Networkscited by 809
- 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
- 2018PU-Net: Point Cloud Upsampling Networkcited by 209
- 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 164
- 2018Patch-based Progressive 3D Point Set Upsamplingcited by 101
- 2019PU-GCN: Point Cloud Upsampling using Graph Convolutional Networkscited by 101
- 2018EC-Net: an Edge-aware Point set Consolidation Networkcited by 84
- 2020PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsamplingcited by 69
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