P2P-NET: Bidirectional Point Displacement Net for Shape Transform

By Kang Yin, Simon Fraser

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yifan Wang
2. Daniel Cohen-Or
3. Shihao Wu
4. Olga Sorkine-Hornung
5. Hao Zhang
6. Hui Huang
7. Jiwen Lu
8. Shiming Xiang

Citations in the corpus

• Learning Implicit Fields for Generative Shape Modeling (2018, background): “There have been a variety of 3D shape representations for deep learning of shapes, such as voxel grids [9, 14, 28, 45, 46], octrees [18, 35, 40, 43, 44], multi-view images [29, 39], point clouds [1, 12, 13, 32, 33, 47, 48], geometry images [37, 38], deformable mesh/patches [16, 38, 42, 47], and part”
• DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (2019, background): “, regular grid data, to irregular point cloud processing [34, 19, 35, 49, 59, 27].”
• Patch-Based Progressive 3D Point Set Upsampling (2018, methodology): “It has been successfully applied in point set segmentation [10, 40], generation [1,13,56], consolidation [14,45,58], deformation [57], completion [15, 60] and upsampling [58, 59, 61].”