no code implementations • 11 Apr 2018 • Ganesh Iyer, J. Krishna Murthy, Gunshi Gupta, K. Madhava Krishna, Liam Paull
We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels.
1 code implementation • 22 Mar 2018 • Ganesh Iyer, R. Karnik Ram., J. Krishna Murthy, K. Madhava Krishna
During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i. e., we do not directly regress to the calibration parameters, for example).
4 code implementations • 6 Mar 2018 • Junaid Ahmed Ansari, Sarthak Sharma, Anshuman Majumdar, J. Krishna Murthy, K. Madhava Krishna
The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
1 code implementation • 26 Feb 2018 • Sarthak Sharma, Junaid Ahmed Ansari, J. Krishna Murthy, K. Madhava Krishna
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios.
Ranked #2 on 3D Multi-Object Tracking on KITTI
no code implementations • 26 Feb 2018 • Parv Parkhiya, Rishabh Khawad, J. Krishna Murthy, Brojeshwar Bhowmick, K. Madhava Krishna
These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework.
no code implementations • 29 Sep 2016 • J. Krishna Murthy, G. V. Sai Krishna, Falak Chhaya, K. Madhava Krishna
We then formulate a shape-aware adjustment problem that uses the learnt shape priors to recover the 3D pose and shape of a query object from an image.