用单个全景摄像头实现高精度鸟瞰图地图，降低自动驾驶硬件成本。
Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving
- 仅用单个全景相机+优化采样策略生成鸟瞰图特征。
- 在自建数据集上达到与多摄像头方案相当的分割性能。
- 适合关注低成本感知系统的研究者和工程师。
我们提出Dur360BEV，一个配备128通道高分辨率3D LiDAR和RTK精化GNSS/INS系统的全景摄像头自动驾驶数据集，并设计了仅依赖单个全景相机生成鸟瞰图（BEV）地图的基准架构。该数据集和基准解决自动驾驶中鸟瞰图生成的挑战，通过采用单个360度摄像头替代多个视角相机，显著降低硬件复杂度。在基准架构中，我们提出一种新颖的球面图像到鸟瞰图模块，利用球面图像并结合优化采样策略，实现2D到3D特征投影。此外，创新性地引入适配聚焦损失（focal loss），有效缓解鸟瞰图分割任务中的极端类别不平衡问题，在Dur360BEV数据集上展现出更优的分割表现。实验表明，该方法不仅简化了传感器配置，还实现了具有竞争力的性能。
原文摘要 · Abstract (English)
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.
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