Fangchang Ma
Title
Cited by
Cited by
Year
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
2942018
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
2122019
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
1112019
Invertibility of convolutional generative networks from partial measurements
F Ma, U Ayaz, S Karaman
Neural Information Processing Systems Foundation, Inc., 2019
532019
Sparse sensing for resource-constrained depth reconstruction
F Ma, L Carlone, U Ayaz, S Karaman
2016 IEEE/RSJ International Conference on Intelligent Robots and Systemsá…, 2016
252016
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
152019
Maximum-reward motion in a stochastic environment: The nonequilibrium statistical mechanics perspective
F Ma, S Karaman
Algorithmic Foundations of Robotics XI, 389-406, 2015
22015
Algorithms for single-view depth image estimation
F Ma
Massachusetts Institute of Technology, 2019
12019
Supplementary materials-invertibility of convolutional generative networks from partial measurements
F Ma, U Ayaz, S Karaman
NIPS, 2018
12018
On Sensing, Agility, and Computation Requirements for a Data-gathering Agile Robotic Vehicle
F Ma, S Karaman
arXiv preprint arXiv:1704.02075, 2017
12017
RetrievalFuse: Neural 3D Scene Reconstruction with a Database
Y Siddiqui, J Thies, F Ma, Q Shan, M Nie▀ner, A Dai
arXiv preprint arXiv:2104.00024, 2021
2021
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
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Articles 1–13