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Found 6 papers, 0 papers with code
Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?
A visual comparison between the URes34P model developed in this work and the existing state of the art in deep learning image enhancement methods, relevant to images captured in space, is presented.
Self-Supervised Learning for Place Representation Generalization across Appearance Changes
Visual place recognition is a key to unlocking spatial navigation for animals, humans and robots.
3D-Aware Object Localization using Gaussian Implicit Occupancy Function
We then propose to have a new look at ellipse regression and replace the discontinuous geometric ellipse parameters with the parameters of an implicit Gaussian distribution encoding object occupancy in the image.
Leveraging Equivariant Features for Absolute Pose Regression
While end-to-end approaches have achieved state-of-the-art performance in many perception tasks, they are not yet able to compete with 3D geometry-based methods in pose estimation.
LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network
Being capable of estimating the pose of uncooperative objects in space has been proposed as a key asset for enabling safe close-proximity operations such as space rendezvous, in-orbit servicing and active debris removal.
SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment
This paper proposes the SPARK dataset as a new unique space object multi-modal image dataset.
