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Found 7 papers, 2 papers with code
Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images
Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application.
On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation
Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant.
3D Pose Estimation
Weakly-supervised 3D Human Pose Estimation
+1
Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate
Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks.
Promoting Connectivity of Network-Like Structures by Enforcing Region Separation
The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image.
TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation
Instead, we use a more sophisticated discriminator that returns a label pyramid describing what portions of the road network are correct at several different scales.
Real-Time Camera Pose Estimation for Sports Fields
Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.
Towards Reliable Evaluation of Road Network Reconstructions
Existing performance measures rank delineation algorithms inconsistently, which makes it difficult to decide which one is best in any given situation.
