Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
We present Dur360BEV, a novel spherical camera autonomous driving dataset
equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS
system, along with a benchmark architecture designed to generate Bird-Eye-View
(BEV) maps using only a single spherical camera. This dataset and benchmark
address the challenges of BEV generation in autonomous driving, particularly by
reducing hardware complexity through the use of a single 360-degree camera
instead of multiple perspective cameras. Within our benchmark architecture, we
propose a novel spherical-image-to-BEV module that leverages spherical imagery
and a refined sampling strategy to project features from 2D to 3D. Our approach
also includes an innovative application of focal loss, specifically adapted to
address the extreme class imbalance often encountered in BEV segmentation
tasks, that demonstrates improved segmentation performance on the Dur360BEV
dataset. The results show that our benchmark not only simplifies the sensor
setup but also achieves competitive performance.
