PU-Net: Point Cloud Upsampling Network
Lequan Yu,Xianzhi Li,Chi-Wing Fu,Daniel Cohen-Or,Pheng-Ann Heng +4 more
- 01 Jun 2018
- pp 2790-2799
TL;DR: A data-driven point cloud upsampling technique to learn multi-level features per point and expand the point set via a multi-branch convolution unit implicitly in feature space, which shows that its upsampled points have better uniformity and are located closer to the underlying surfaces.
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Abstract: Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud upsampling technique. The key idea is to learn multi-level features per point and expand the point set via a multi-branch convolution unit implicitly in feature space. The expanded feature is then split to a multitude of features, which are then reconstructed to an upsampled point set. Our network is applied at a patch-level, with a joint loss function that encourages the upsampled points to remain on the underlying surface with a uniform distribution. We conduct various experiments using synthesis and scan data to evaluate our method and demonstrate its superiority over some baseline methods and an optimization-based method. Results show that our upsampled points have better uniformity and are located closer to the underlying surfaces.
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Citations
Pointwise Convolutional Neural Networks
Binh-Son Hua,Minh-Khoi Tran,Sai-Kit Yeung +2 more
- 18 Jun 2018
TL;DR: Pointwise convolution as discussed by the authors is a new convolution operator that can be applied at each point of a point cloud, which can yield competitive accuracy in both semantic segmentation and object recognition task.
ABC: A Big CAD Model Dataset for Geometric Deep Learning
Sebastian Koch,Albert Matveev,Zhongshi Jiang,Francis Williams,Alexey Artemov,Evgeny Burnaev,Marc Alexa,Denis Zorin,Daniele Panozzo +8 more
- 01 Jun 2019
TL;DR: This work performs a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
Haowen Deng,Tolga Birdal,Slobodan Ilic +2 more
- 08 Sep 2018
TL;DR: It is demonstrated that despite having six degree-of-freedom invariance and lack of training labels, PPF-FoldNet achieves state of the art results in standard benchmark datasets and outperforms its competitors when rotations and varying point densities are present.
PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows
Guandao Yang,Xun Huang,Zekun Hao,Ming-Yu Liu,Serge Belongie,Bharath Hariharan +5 more
- 28 Jun 2019
TL;DR: PointFlow as discussed by the authors proposes a principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions, where the first level is the distribution of shapes and the second level is given a shape.
3D Point Capsule Networks
Yongheng Zhao,Tolga Birdal,Haowen Deng,Federico Tombari +3 more
- 15 Jun 2019
TL;DR: 3D point-capsule networks as mentioned in this paper is an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data, which enables new applications such as part interpolation and replacement.
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