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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments
Publication Type:
Conference/Workshop Paper
Venue:
43rd Digital Avionics Systems Conference (DASC)
Abstract
Machine Learning (ML) systems require representative
and diverse datasets to accurately learn the objective task.
In supervised learning data needs to be accurately annotated,
which is an expensive and error-prone process. We present
a method for generating synthetic data tailored to the usecase
achieving excellent performance in a real-world usecase.
We provide a method for producing automatically annotated
synthetic visual data of multirotor unmanned aerial vehicle (UAV)
and other airborne objects in a simulated environment with a
high degree of scene diversity, from collection of 3D models to
generation of annotated synthetic datasets (synthsets). In our
data generation framework SynRender we introduce a novel
method of using Neural Radiance Field (NeRF) to capture photorealistic
high-fidelity 3D-models of multirotor UAVs in order
to automate data generation for an object detection task in
diverse environments. By producing data tailored to the real world
setting, our NeRF-derived results show an advantage over
generic 3D asset collection-based methods where the domain gap
between the simulated and real-world is unacceptably large. In
the spirit of keeping research open and accessible to the research
community we release our dataset VISER DroneDiversity used
in this project, where visual images, annotated boxes, instance
segmentation and depth maps are all generated for each image
sample.
Bibtex
@inproceedings{Linden6972,
author = {Joakim Lind{\'e}n and Giovanni Burresi and H{\aa}kan Forsberg and Masoud Daneshtalab and Ingemar S{\"o}derquist},
title = {Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments},
month = {October},
year = {2024},
booktitle = {43rd Digital Avionics Systems Conference (DASC)},
url = {http://www.es.mdu.se/publications/6972-}
}