Seguir
Rares Ambrus
Rares Ambrus
Toyota Research Institute (TRI)
Dirección de correo verificada de tri.global
Título
Citado por
Citado por
Año
3d packing for self-supervised monocular depth estimation
V Guizilini, R Ambrus, S Pillai, A Raventos, A Gaidon
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
3722020
The strands project: Long-term autonomy in everyday environments
N Hawes, C Burbridge, F Jovan, L Kunze, B Lacerda, L Mudrova, J Young, ...
IEEE Robotics & Automation Magazine 24 (3), 146-156, 2017
2042017
Superdepth: Self-supervised, super-resolved monocular depth estimation
S Pillai, R Ambruş, A Gaidon
2019 International Conference on Robotics and Automation (ICRA), 9250-9256, 2019
1822019
Semantically-guided representation learning for self-supervised monocular depth
V Guizilini, R Hou, J Li, R Ambrus, A Gaidon
arXiv preprint arXiv:2002.12319, 2020
1492020
Is pseudo-lidar needed for monocular 3d object detection?
D Park, R Ambrus, V Guizilini, J Li, A Gaidon
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
1042021
Meta-rooms: Building and maintaining long term spatial models in a dynamic world
R Ambruş, N Bore, J Folkesson, P Jensfelt
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2014
752014
Autonomous learning of object models on a mobile robot
T Fäulhammer, R Ambruş, C Burbridge, M Zillich, J Folkesson, N Hawes, ...
IEEE Robotics and Automation Letters 2 (1), 26-33, 2016
712016
Automatic room segmentation from unstructured 3-D data of indoor environments
R Ambruş, S Claici, A Wendt
IEEE Robotics and Automation Letters 2 (2), 749-756, 2017
682017
Modeling motion patterns of dynamic objects by IOHMM
Z Wang, R Ambrus, P Jensfelt, J Folkesson
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2014
442014
Probabilistic 3d multi-modal, multi-object tracking for autonomous driving
H Chiu, J Li, R Ambruş, J Bohg
2021 IEEE International Conference on Robotics and Automation (ICRA), 14227 …, 2021
432021
Augmented autonomy: Improving human-robot team performance in urban search and rescue
Y Nevatia, T Stoyanov, R Rathnam, M Pfingsthorn, S Markov, R Ambrus, ...
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2008
402008
Where's waldo at time t? using spatio-temporal models for mobile robot search
T Krajník, M Kulich, L Mudrová, R Ambrus, T Duckett
2015 IEEE International Conference on Robotics and Automation (ICRA), 2140-2146, 2015
382015
Robust semi-supervised monocular depth estimation with reprojected distances
V Guizilini, J Li, R Ambrus, S Pillai, A Gaidon
Conference on robot learning, 503-512, 2020
352020
Packnet-sfm: 3d packing for self-supervised monocular depth estimation
V Guizilini, R Ambrus, S Pillai, A Gaidon
arXiv preprint arXiv:1905.02693 5, 1, 2019
282019
Intelligent robotic perception systems
C Premebida, R Ambrus, ZC Marton
Applications of Mobile Robots, 111-127, 2018
282018
Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario
R Ambrus, J Ekekrantz, J Folkesson, P Jensfelt
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
282015
Semantic labeling of indoor environments from 3d rgb maps
M Brucker, M Durner, R Ambruş, ZC Márton, A Wendt, P Jensfelt, ...
2018 IEEE International Conference on Robotics and Automation (ICRA), 1871-1878, 2018
252018
Self-supervised 3d keypoint learning for ego-motion estimation
J Tang, R Ambrus, V Guizilini, S Pillai, H Kim, P Jensfelt, A Gaidon
Conference on Robot Learning, 2085-2103, 2021
232021
Sparse auxiliary networks for unified monocular depth prediction and completion
V Guizilini, R Ambrus, W Burgard, A Gaidon
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
222021
Neural ray surfaces for self-supervised learning of depth and ego-motion
I Vasiljevic, V Guizilini, R Ambrus, S Pillai, W Burgard, G Shakhnarovich, ...
2020 International Conference on 3D Vision (3DV), 1-11, 2020
142020
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20