文章目录
研究旋转不变就从这里开始吧.
【3DV 2019】Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
【IJCV 2022】RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Relete work 关于旋转不变的：
Rao, Y., Lu, J., & Zhou, J. (2019). Spherical fractal convolutional neural networks for point cloud recognition. In Computer vision and pattern recognition
Poulenard, A., Rakotosaona, M.J., Ponty, Y., & Ovsjanikov, M. (2019). Effective rotation-invariant point cnn with spherical harmonics kernels. In International conference on 3D VisionPoulenard, A., Rakotosaona, M.J., Ponty, Y., & Ovsjanikov, M. (2019). Effective rotation-invariant point cnn with spherical harmonics kernels. In International conference on 3D Vision
Chen, C., Li, G., Xu, R., Chen, T., Wang, M., & Lin, L. (2019). Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 4994–5002).
Zhang, Z., Hua, B.S., Rosen, D.W., Yeung, S.K. (2019a). Rotation invariant convolutions for 3d point clouds deep learning. In International conference on 3D Vision, (pp. 204–213)
Zhang, Z., Hua, B.S., Chen, W., Tian, Y., & Yeung, S.K. (2020). Global context aware convolutions for 3d point cloud understanding. In International conference on 3D vision
扫描二维码关注公众号，回复： 14176733 查看本文章
Kim, S., Park, J., & Han, B. (2020b). Rotation-invariant local-to-global representation learning for 3d point cloud. Advances in Neural Information Processing Systems, 33, 8174–8185.
Thomas, H. (2020). Rotation-invariant point convolution with multiple equivariant alignments. In 2020 International Conference on 3D Vision (3DV), (pp. 504–513).
Li, X., Li, R., Chen, G., Fu, C.W., Cohen-Or, D., & Heng, P.A. (2021). A rotation-invariant framework for deep point cloud analysis. IEEE Transactions on Visualization and Computer Graphics. https://doi. org/10.48550/arXiv.2003.07238
