Depth And Camera Motion
13 papers with code • 0 benchmarks • 1 datasets
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CamlessVideosFromTheWild
Latest papers with no code
Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography
Modern mobile burst photography pipelines capture and merge a short sequence of frames to recover an enhanced image, but often disregard the 3D nature of the scene they capture, treating pixel motion between images as a 2D aggregation problem.
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion
In this paper, we show that effective feature-level collaboration of the networks for the three respective tasks could achieve much greater performance improvement for all three tasks than only loss-level joint optimization.
3D Video Stabilization With Depth Estimation by CNN-Based Optimization
We take advantage of the recent self-supervised framework on jointly learning depth and camera ego-motion estimation on raw videos.
Unsupervised Learning of Camera Pose with Compositional Re-estimation
We consider the problem of unsupervised camera pose estimation.
Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment
Learning to predict scene depth and camera motion from RGB inputs only is a challenging task.
Learning Residual Flow as Dynamic Motion from Stereo Videos
Based on rigid projective geometry, the estimated stereo depth is used to guide the camera motion estimation, and the depth and camera motion are used to guide the residual flow estimation.
Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus
Unsupervised learning based depth estimation methods have received more and more attention as they do not need vast quantities of densely labeled data for training which are touch to acquire.
Enhancing self-supervised monocular depth estimation with traditional visual odometry
Estimating depth from a single image represents an attractive alternative to more traditional approaches leveraging multiple cameras.
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion.
Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach
Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features.
