| Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers J Tachella, Y Altmann, N Mellado, A Mccarthy, R Tobin, GS Buller, ... Nature Communications 10, 2019 | 224 | 2019 |
| Advances in single-photon lidar for autonomous vehicles: Working principles, challenges, and recent advances J Rapp, J Tachella, Y Altmann, S McLaughlin, VK Goyal IEEE Signal Processing Magazine 37 (4), 62-71, 2020 | 140 | 2020 |
| Equivariant Imaging: Learning Beyond the Range Space D Chen, J Tachella, ME Davies Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 110 | 2021 |
| Bayesian 3D reconstruction of complex scenes from single-photon lidar data J Tachella, Y Altmann, X Ren, A McCarthy, GS Buller, S McLaughlin, ... SIAM Journal on Imaging Sciences 12 (1), 521-550, 2019 | 100 | 2019 |
| Robust equivariant imaging: a fully unsupervised framework for learning to image from noisy and partial measurements D Chen, J Tachella, ME Davies Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 69 | 2022 |
| Seeing Around Corners with Edge-Resolved Transient Imaging J Rapp, C Saunders, J Tachella, J Murray-Bruce, Y Altmann, JY Tourneret, ... Nature Communications 11 (5929), 2020 | 66 | 2020 |
| Submerged single-photon LiDAR imaging sensor used for real-time 3D scene reconstruction in scattering underwater environments A Maccarone, K Drummond, A McCarthy, UK Steinlehner, J Tachella, ... Optics Express 31 (10), 16690-16708, 2023 | 60 | 2023 |
| The neural tangent link between CNN denoisers and non-local filters J Tachella, J Tang, M Davies Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 38* | 2021 |
| Sensing Theorems for Unsupervised Learning in Linear Inverse Problems J Tachella, D Chen, M Davies Journal of Machine Learning Research 24 (39), 1-45, 2023 | 31* | 2023 |
| Imaging with equivariant deep learning: From unrolled network design to fully unsupervised learning D Chen, M Davies, MJ Ehrhardt, CB Schönlieb, F Sherry, J Tachella IEEE Signal Processing Magazine 40 (1), 134-147, 2023 | 30 | 2023 |
| A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar MP Sheehan, J Tachella, ME Davies IEEE Transactions on Computational Imaging, 2021 | 27 | 2021 |
| Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals J Tachella, Y Altmann, M Márquez, H Arguello-Fuentes, JY Tourneret, ... IEEE Transactions on Computational Imaging, 2019 | 27 | 2019 |
| Unsupervised Learning From Incomplete Measurements for Inverse Problems J Tachella, D Chen, M Davies NeurIPS 2022, 2022 | 24 | 2022 |
| 3D reconstruction using single-photon lidar data exploiting the widths of the returns J Tachella, Y Altmann, S McLaughlin, JY Tourneret ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 21 | 2019 |
| Robust 3d reconstruction of dynamic scenes from single-photon lidar using beta-divergences Q Legros, J Tachella, R Tobin, A McCarthy, S Meignen, GS Buller, ... IEEE Transactions on Image Processing 30, 1716-1727, 2020 | 18 | 2020 |
| Equivariant plug-and-play image reconstruction M Terris, T Moreau, N Pustelnik, J Tachella Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 15 | 2024 |
| Fast surface detection in single-photon lidar waveforms J Tachella, Y Altmann, S McLaughlin, JY Tourneret 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 12 | 2019 |
| DeepInverse: A deep learning framework for inverse problems in imaging J Tachella, D Chen, S Hurault, M Terris, A Wang URL: https://deepinv. github. io/deepinv, 2023 | 10 | 2023 |
| On fast object detection using single-photon lidar data J Tachella, Y Altmann, S McLaughlin, JY Tourneret Wavelets and Sparsity XVIII 11138, 252-261, 2019 | 10 | 2019 |
| Equivariant bootstrapping for uncertainty quantification in imaging inverse problems J Tachella, M Pereyra 21st International Conference on Artificial Intelligence and Statistics …, 2024 | 8 | 2024 |
Stephen McLaughlinHeriot-Watt UniversityDirección de correo verificada de hw.ac.uk
