| Adversarial learning and self-teaching techniques for domain adaptation in semantic segmentation U Michieli, M Biasetton, G Agresti, P Zanuttigh IEEE Transactions on Intelligent Vehicles 5 (3), 508-518, 2020 | 59 | 2020 |
| Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment M Toldo, U Michieli, G Agresti, P Zanuttigh Image and Vision Computing 95, 103889, 2020 | 49 | 2020 |
| Unsupervised domain adaptation for tof data denoising with adversarial learning G Agresti, H Schaefer, P Sartor, P Zanuttigh Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 48 | 2019 |
| Unsupervised domain adaptation for semantic segmentation of urban scenes M Biasetton, U Michieli, G Agresti, P Zanuttigh Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 47 | 2019 |
| Deep learning for confidence information in stereo and tof data fusion G Agresti, L Minto, G Marin, P Zanuttigh Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 44 | 2017 |
| Deep learning for multi-path error removal in ToF sensors G Agresti, P Zanuttigh Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018 | 37 | 2018 |
| Combination of spatially-modulated ToF and structured light for MPI-free depth estimation G Agresti, P Zanuttigh Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018 | 26 | 2018 |
| Confidence estimation for ToF and stereo sensors and its application to depth data fusion M Poggi, G Agresti, F Tosi, P Zanuttigh, S Mattoccia IEEE Sensors Journal 20 (3), 1411-1421, 2019 | 24 | 2019 |
| Material identification using RF sensors and convolutional neural networks G Agresti, S Milani ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 18 | 2019 |
| Deep learning for transient image reconstruction from ToF data E Buratto, A Simonetto, G Agresti, H Schäfer, P Zanuttigh Sensors 21 (6), 1962, 2021 | 15 | 2021 |
| Stereo and ToF data fusion by learning from synthetic data G Agresti, L Minto, G Marin, P Zanuttigh Information Fusion 49, 161-173, 2019 | 15 | 2019 |
| A multi-camera dataset for depth estimation in an indoor scenario G Marin, G Agresti, L Minto, P Zanuttigh Data in brief 27, 104619, 2019 | 13 | 2019 |
| Unsupervised domain adaptation of deep networks for ToF depth refinement G Agresti, H Schäfer, P Sartor, Y Incesu, P Zanuttigh IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12), 9195 …, 2021 | 6 | 2021 |
| Lightweight deep learning architecture for MPI correction and transient reconstruction A Simonetto, G Agresti, P Zanuttigh, H Schäfer IEEE Transactions on Computational Imaging 8, 721-732, 2022 | 4 | 2022 |
| A low memory footprint quantized neural network for depth completion of very sparse time-of-flight depth maps X Jiang, V Cambareri, G Agresti, CI Ugwu, A Simonetto, F Cardinaux, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 3 | 2022 |
| A rate control algorithm for video coding in augmented reality applications S Milani, G Agresti, G Calvagno 2016 Picture Coding Symposium (PCS), 1-5, 2016 | 2 | 2016 |
| Apparatuses and methods for training a machine learning network for use with a time-of-flight camera H Schäfer, E Buratto, G Agresti, P Zanuttigh US Patent App. 17/110,330, 2021 | 1 | 2021 |
| NIGHT--Non-Line-of-Sight Imaging from Indirect Time of Flight Data M Caligiuri, A Simonetto, G Agresti, P Zanuttigh arXiv preprint arXiv:2403.19376, 2024 | | 2024 |
| Exploiting Multiple Priors for Neural 3D Indoor Reconstruction F Lincetto, G Agresti, M Rossi, P Zanuttigh arXiv preprint arXiv:2309.07021, 2023 | | 2023 |
| Time-of-flight simulation data training circuitry, time-of-flight simulation data training method, time-of-flight simulation data output method, time-of-flight simulation data … G Agresti, H Schäfer, Y Incesu, P Sartor, P Zanuttigh US Patent App. 17/527,191, 2022 | | 2022 |
Pietro ZanuttighUniversity of PadovaVerified email at dei.unipd.it
