Bayesian modeling of uncertainty in low-level vision
International Journal of Computer Vision
A Kalman Filter Approach to Direct Depth Estimation Incorporating Surface Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
| Hi-index | 0.00 |
Monocular depth has been found using estimation, closed-form solution and learning techniques. Estimation and closed-form solution compute the depth from motion, while learning techniques calculate the depth using a single image with a depth map as a supervisor. This paper presents a new closed form solution for monocular depth from motion. The proposed method builds on the notation that an interest point in an image of a static scene has a static world location. Camera pose and calibration parameters are used as constraints to provide the depth solution. The proposed method is verified through real experiments on indoor mobile robot platform. The effect of uncertainty in the solution variables is studied and the results are benchmarked to groundtruth.