5. PointNet
5
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017). PointNet : Deep
Learning on Point Sets for 3D Classification and Segmentation
Big Data + Deep Representation Learning. IEEE Conference on
ComputerVision and Pattern Recognition (CVPR).
各点群の点を独立に畳み込む
Global Max Poolingで点群全体の特徴量を取得
各点を個別
に畳み込み
アフィン変換
各点の特徴を統合
6. PointNet++
6
Qi, C. R.,Yi, L., Su, H., & Guibas, L. J. (2017). PointNet++: Deep
Hierarchical Feature Learning on Point Sets in a Metric Space.
Conference on Neural Information Processing Systems (NIPS).
PointNetを階層的に適用
点群をクラスタ分割→PointNet→クラスタ内で統合を繰り返す
14. CVPR2018で紹介された点群畳み込み研究
14
1. Hua, B.-S.,Tran, M.-K., &Yeung, S.-K.“Pointwise Convolutional Neural
Networks”
2. Le,T., & Duan,Y. “PointGrid:A Deep Network for 3D Shape
Understanding”
3. Huang, Q.,Wang,W., & Neumann,“U. Recurrent Slice Networks for 3D
Segmentation of Point Clouds”
4. Li, J., Chen, B. M., & Lee, G. H.“SO-Net: Self-Organizing Network for
Point Cloud Analysis”
5. Shen,Y., Feng, C.,Yang,Y., &Tian, D.“Mining Point Cloud Local Structures
by Kernel Correlation and Graph Pooling”
6. Liu, S., Xie, S., Chen, Z., &Tu, Z.“Attentional ShapeContextNet for Point
Cloud Recognition”
7. Tatarchenko, M., Park, J., Koltun,V., & Zhou, Q. “Tangent convolutions for
dense prediction in 3D”
8. Wang, S., Suo, S., Ma,W., & Urtasun, R. “Deep Parametric Continuous
Convolutional Neural Networks”
9. Su, H., Jampani,V., Sun, D., Maji, S., Kalogerakis, E.,Yang, M.-H., & Kautz, J.
“SPLATNet: Sparse Lattice Networks for Point Cloud Processing”