Chinese Optics Letters, Volume. 23, Issue 5, 051102(2025)
Tactile-assisted point cloud super-resolution
[1] H. Liu, J. Luo, P. Wu et al. People perception from rgb-d cameras for mobile robots. 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2020(2015).
[6] C. R. Qi, H. Su, K. Mo et al. PointNet: deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 652(2017).
[7] C. R. Qi, L. Yi, H. Su et al. PointNet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems(2017).
[9] H. Su, V. Jampani, D. Sun et al. Splatnet: Sparse lattice networks for point cloud processing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2530(2018).
[10] Q. Hu, B. Yang, L. Xie et al. RandLA-Net: Efficient semantic segmentation of large-scale point clouds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11108(2020).
[11] C. R. Qi, X. Chen, O. Litany et al. ImVoteNet: boosting 3D object detection in point clouds with image votes. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4404(2020).
[12] Y. Zhou, O. Tuzel. VoxelNet: end-to-end learning for point cloud based 3D object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4490(2018).
[13] N. Carion, F. Massa, G. Synnaeve et al. End-to-end object detection with transformers. European Conference on Computer Vision, 213(2020).
[16] M. Björkman, Y. Bekiroglu, V. Högman et al. Enhancing visual perception of shape through tactile glances. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3180(2013).
[19] E. Smith, R. Calandra, A. Romero et al. 3D shape reconstruction from vision and touch. 34th Conference on Neural Information Processing Systems(2020).
[20] E. Smith, D. Meger, L. Pineda et al. Active 3D shape reconstruction from vision and touch. 35th Conference on Neural Information Processing Systems(2021).
[23] C. Dong, C. C. Loy, K. He et al. Learning a deep convolutional network for image super-resolution. Computer Vision–ECCV 2014: 13th European Conference, 184(2014).
[24] B. Lim, S. Son, H. Kim et al. Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 136(2017).
[25] Y. Zhang, K. Li, K. Li et al. Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), 286(2018).
[26] Y. Zhang, H. Wang, C. Qin et al. Aligned structured sparsity learning for efficient image super-resolution. Advances in Neural Information Processing Systems(2021).
[27] R. Li, X. Li, C.-W. Fu et al. PU-GAN: a point cloud upsampling adversarial network. Proceedings of the IEEE/CVF International Conference on Computer Vision, 7203(2019).
[28] G. Qian, A. Abualshour, G. Li et al. PU-GCN: point cloud upsampling using graph convolutional networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11683(2021).
[29] S. Qiu, S. Anwar, N. Barnes. PU-Transformer: point cloud upsampling transformer. Proceedings of the Asian Conference on Computer Vision, 2475(2022).
[30] R. Li, X. Li, P.-A. Heng et al. Point cloud upsampling via disentangled refinement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 344(2021).
[31] W. Shi, J. Caballero, F. Huszar et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016).
[35] Y. Li, R. Bu, M. Sun et al. PointCNN: convolution on x-transformed points. Advances in Neural Information Processing Systems(2018).
[36] L. Yu, X. Li, C.-W. Fu et al. PU-Net: point cloud upsampling network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2790(2018).
[37] W. Yifan, S. Wu, H. Huang et al. Patch-based progressive 3D point set upsampling. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5958(2019).
[38] S. Wang, J. Wu, X. Sun et al. 3D shape perception from monocular vision, touch, and shape priors. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1606(2018).
[40] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, 234(2015).
[42] S. Koch, A. Matveev, Z. Jiang et al. ABC: a big CAD model dataset for geometric deep learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9601(2019).
[43] Y. He, D. Tang, Y. Zhang et al. Grad-PU: arbitrary-scale point cloud upsampling via gradient descent with learned distance functions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5354(2023).
[44] X. Ma, Y. Zhou, H. Wang et al. Image as set of points. The Eleventh International Conference on Learning Representations(2023).
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Haoran Shen, Puzheng Wang, Ming Lu, Chi Zhang, Jian Li, Qin Wang, "Tactile-assisted point cloud super-resolution," Chin. Opt. Lett. 23, 051102 (2025)
Category: Imaging Systems and Image Processing
Received: Jul. 5, 2024
Accepted: Nov. 14, 2024
Published Online: May. 14, 2025
The Author Email: Jian Li (jianli@njupt.edu.cn), Qin Wang (qinw@njupt.edu.cn)