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Found 6 papers, 3 papers with code
DF-VO: What Should Be Learnt for Visual Odometry?
More surprisingly, they show that the well-trained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.
Visual Odometry Revisited: What Should Be Learnt?
In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning.
Self-supervised Learning for Single View Depth and Surface Normal Estimation
In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single image.
Ranked #62 on
Monocular Depth Estimation
on KITTI Eigen split
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image.
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images.
