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Found 5 papers, 2 papers with code
Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM
In addition, we compare our online adaptation framework against the state-of-the-art pre-trained depth prediction CNNs to show that our online adapted depth prediction CNN outperforms the depth prediction CNNs that have been trained on a large collection of datasets.
Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior
This paper is concerned with polarimetric dense map reconstruction based on a polarization camera with the help of relative depth information as a prior.
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction
To this end, we use a visual SLAM algorithm to reliably recover the camera poses and semi-dense depth maps of the keyframes, and then use relative depth prediction to densify the semi-dense depth maps and refine the keyframe pose-graph.
Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network
In general, semi-supervised training is preferred as it does not suffer from the weaknesses of either supervised training, resulting from the difference in the cameras and the LiDARs field of view, or unsupervised training, resulting from the poor depth accuracy that can be recovered from a stereo pair.
Ranked #50 on
Monocular Depth Estimation
on KITTI Eigen split
CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction
Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms.
