Sparse-to-dense: Depth prediction from sparse depth samples and a single image F Ma, S Karaman 2018 IEEE International Conference on Robotics and Automation (ICRA), 1-8, 2018 | 224 | 2018 |
Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera F Ma, GV Cavalheiro, S Karaman 2019 International Conference on Robotics and Automation (ICRA), 3288-3295, 2019 | 124 | 2019 |
Fastdepth: Fast monocular depth estimation on embedded systems D Wofk, F Ma, TJ Yang, S Karaman, V Sze 2019 International Conference on Robotics and Automation (ICRA), 6101-6108, 2019 | 52 | 2019 |
Invertibility of convolutional generative networks from partial measurements F Ma, U Ayaz, S Karaman Advances in Neural Information Processing Systems, 9628-9637, 2018 | 27 | 2018 |
Sparse sensing for resource-constrained depth reconstruction F Ma, L Carlone, U Ayaz, S Karaman 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 23 | 2016 |
Sparse depth sensing for resource-constrained robots F Ma, L Carlone, U Ayaz, S Karaman The International Journal of Robotics Research 38 (8), 935-980, 2019 | 12 | 2019 |
Maximum-reward motion in a stochastic environment: The nonequilibrium statistical mechanics perspective F Ma, S Karaman Algorithmic Foundations of Robotics XI, 389-406, 2015 | 2 | 2015 |
Algorithms for single-view depth image estimation F Ma Massachusetts Institute of Technology, 2019 | 1 | 2019 |
On Sensing, Agility, and Computation Requirements for a Data-gathering Agile Robotic Vehicle F Ma, S Karaman arXiv preprint arXiv:1704.02075, 2017 | | 2017 |
On maximum-reward motion in stochastic environments F Ma Massachusetts Institute of Technology, 2015 | | 2015 |
Velocity estimator via fusing inertial measurements and multiple feature correspondences from a single camera G Zhou, F Ma, Z Li, T Wang Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on, 2013 | | 2013 |