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Found 6 papers, 2 papers with code
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors
We introduce a solution to large scale Augmented Reality for outdoor scenes by registering camera images to textured Digital Elevation Models (DEMs).
Ranked #2 on
Patch Matching
on HPatches
(using extra training data)
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement
Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy, the first machine-learning-driven labeling method, in contrast to the existing hand-crafted algorithms designed by human experts.
Resource Efficient Mountainous Skyline Extraction using Shallow Learning
We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions.
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection
Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions.
Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation
For large line sets (10 and more), the method is comparable to the state-of-theart in accuracy of orientation estimation.
Camera Pose Estimation from Lines using Plücker Coordinates
Correspondences between 3D lines and their 2D images captured by a camera are often used to determine position and orientation of the camera in space.
