| DeepVO: Towards end-to-end visual odometry with deep recurrent convolutional neural networks S Wang, R Clark, H Wen, N Trigoni Robotics and Automation (ICRA), 2017 IEEE International Conference on, 2043-2050, 2017 | 763 | 2017 |
| End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks S Wang, R Clark, H Wen, N Trigoni The International Journal of Robotics Research 37 (4-5), 513-542, 2018 | 608* | 2018 |
| VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem. R Clark, S Wang, H Wen, A Markham, N Trigoni AAAI, 3995-4001, 2017 | 329 | 2017 |
| CodeSLAM-Learning a Compact, Optimisable Representation for Dense Visual SLAM M Bloesch, J Czarnowski, R Clark, S Leutenegger, AJ Davison IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 310 | 2018 |
| Vidloc: A deep spatio-temporal model for 6-dof video-clip relocalization R Clark, S Wang, A Markham, N Trigoni, H Wen IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6856-6864, 2017 | 280 | 2017 |
| Fusion++: Volumetric object-level slam J McCormac, R Clark, M Bloesch, A Davison, S Leutenegger 2018 international conference on 3D vision (3DV), 32-41, 2018 | 250 | 2018 |
| Learning object bounding boxes for 3D instance segmentation on point clouds B Yang, J Wang, R Clark, Q Hu, S Wang, A Markham, N Trigoni Advances in neural information processing systems 32, 2019 | 241 | 2019 |
| 3D object reconstruction from a single depth view with adversarial learning B Yang, H Wen, S Wang, R Clark, A Markham, N Trigoni arXiv preprint arXiv:1708.07969, 2017 | 185 | 2017 |
| Interiornet: Mega-scale multi-sensor photo-realistic indoor scenes dataset W Li, S Saeedi, J McCormac, R Clark, D Tzoumanikas, Q Ye, Y Huang, ... arXiv preprint arXiv:1809.00716, 2018 | 180 | 2018 |
| Dense 3D object reconstruction from a single depth view B Yang, S Rosa, A Markham, N Trigoni, H Wen IEEE transactions on pattern analysis and machine intelligence 41 (12), 2820 …, 2018 | 131 | 2018 |
| Anomaly detection for time series using vae-lstm hybrid model S Lin, R Clark, R Birke, S Schönborn, N Trigoni, S Roberts ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 117 | 2020 |
| Deepfactors: Real-time probabilistic dense monocular slam J Czarnowski, T Laidlow, R Clark, AJ Davison IEEE Robotics and Automation Letters 5 (2), 721-728, 2020 | 96 | 2020 |
| Learning to Solve Nonlinear Least Squares for Monocular Stereo R Clark, M Bloesch, J Czarnowski, A Davison, S Leutenegger Proceedings of the European Conference on Computer Vision (ECCV), 284-299, 2018 | 76* | 2018 |
| Uncovering latent style factors for expressive speech synthesis Y Wang, RJ Skerry-Ryan, Y Xiao, D Stanton, J Shor, E Battenberg, ... arXiv preprint arXiv:1711.00520, 2017 | 75 | 2017 |
| Keyframe based large-scale indoor localisation using geomagnetic field and motion pattern S Wang, H Wen, R Clark, N Trigoni Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International …, 2016 | 52 | 2016 |
| Improving prosody of RNN-based english text-to-speech synthesis by incorporating a BERT model T Kenter, MK Sharma, R Clark | 32 | 2020 |
| Terminerf: Ray termination prediction for efficient neural rendering M Piala, R Clark 2021 International Conference on 3D Vision (3DV), 1106-1114, 2021 | 25 | 2021 |
| Pointloc: Deep pose regressor for lidar point cloud localization W Wang, B Wang, P Zhao, C Chen, R Clark, B Yang, A Markham, ... IEEE Sensors Journal 22 (1), 959-968, 2021 | 25 | 2021 |
| Scalable uncertainty for computer vision with functional variational inference EDC Carvalho, R Clark, A Nicastro, PHJ Kelly Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 15 | 2020 |
| Learning meshes for dense visual slam M Bloesch, T Laidlow, R Clark, S Leutenegger, AJ Davison Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 15 | 2019 |
Niki TrigoniUniversity of OxfordBestätigte E-Mail-Adresse bei cs.ox.ac.uk
