| Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather M Bijelic, T Gruber, F Mannan, F Kraus, W Ritter, K Dietmayer, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 456 | 2020 |
| Neural scene graphs for dynamic scenes J Ost, F Mannan, N Thuerey, J Knodt, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 247 | 2021 |
| Seeing around street corners: Non-line-of-sight detection and tracking in-the-wild using doppler radar N Scheiner, F Kraus, F Wei, B Phan, F Mannan, N Appenrodt, W Ritter, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 132 | 2020 |
| Differentiable compound optics and processing pipeline optimization for end-to-end camera design E Tseng, A Mosleh, F Mannan, K St-Arnaud, A Sharma, Y Peng, A Braun, ... ACM Transactions on Graphics (TOG) 40 (2), 1-19, 2021 | 96 | 2021 |
| Steady-state non-line-of-sight imaging W Chen, S Daneau, F Mannan, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 81 | 2019 |
| Hyperparameter optimization in black-box image processing using differentiable proxies. E Tseng, F Yu, Y Yang, F Mannan, KST Arnaud, D Nowrouzezahrai, ... ACM Trans. Graph. 38 (4), 27:1-27:14, 2019 | 76 | 2019 |
| Hardware-in-the-loop end-to-end optimization of camera image processing pipelines A Mosleh, A Sharma, E Onzon, F Mannan, N Robidoux, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 61 | 2020 |
| Neural auto-exposure for high-dynamic range object detection E Onzon, F Mannan, F Heide Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 52 | 2021 |
| Seeing through fog without seeing fog: Deep sensor fusion in the absence of labeled training data M Bijelic, F Mannan, T Gruber, W Ritter, K Dietmayer, F Heide arXiv preprint arXiv:1902.08913 1030, 2019 | 48 | 2019 |
| Blur calibration for depth from defocus F Mannan, MS Langer 2016 13th Conference on Computer and Robot Vision (CRV), 281-288, 2016 | 33 | 2016 |
| Adversarial imaging pipelines B Phan, F Mannan, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 25 | 2021 |
| Visibility in three-dimensional cluttered scenes MS Langer, F Mannan JOSA A 29 (9), 1794-1807, 2012 | 21 | 2012 |
| What is a good model for depth from defocus? F Mannan, MS Langer 2016 13th Conference on Computer and Robot Vision (CRV), 273-280, 2016 | 20 | 2016 |
| The differentiable lens: Compound lens search over glass surfaces and materials for object detection G Côté, F Mannan, S Thibault, JF Lalonde, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 14 | 2023 |
| Gated2gated: Self-supervised depth estimation from gated images A Walia, S Walz, M Bijelic, F Mannan, F Julca-Aguilar, M Langer, W Ritter, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 13 | 2022 |
| Pix2shape: Towards unsupervised learning of 3d scenes from images using a view-based representation S Rajeswar, F Mannan, F Golemo, J Parent-Lévesque, D Vazquez, ... International Journal of Computer Vision 128, 2478-2493, 2020 | 12 | 2020 |
| Gated3d: Monocular 3d object detection from temporal illumination cues F Julca-Aguilar, J Taylor, M Bijelic, F Mannan, E Tseng, F Heide Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 11 | 2021 |
| Optimal camera parameters for depth from defocus F Mannan, MS Langer 2015 International Conference on 3D Vision, 326-334, 2015 | 11 | 2015 |
| Classification of face image based on gender using dimensionality reduction techniques and SVM F Mannan McGill University, 2008 | 11 | 2008 |
| Gated stereo: Joint depth estimation from gated and wide-baseline active stereo cues S Walz, M Bijelic, A Ramazzina, A Walia, F Mannan, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 5 | 2023 |
Felix HeidePrinceton University | Torc RoboticsVerified email at cs.princeton.edu
