Depth And Camera Motion
13 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Depth And Camera Motion
Latest papers
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented Reality
Slow adoption of depth information in the UX layer may be due to the complexity of processing depth data to simply render a mesh or detect interaction based on changes in the depth map.
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry
In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of optical flow.
Improving Self-Supervised Single View Depth Estimation by Masking Occlusion
In this work we introduce occlusion mask, a mask that during training can be used to specifically ignore regions that cannot be reconstructed due to occlusions.
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.
BA-Net: Dense Bundle Adjustment Network
The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.
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.
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.