解决鱼眼相机3D目标检测难题，提升精度6.2%。
Exploring Surround-View Fisheye Camera 3D Object Detection
- 用球面表示法建模鱼眼图像几何特性
- 在新数据集Fisheye3DOD上提升精度6.2%
- 适合自动驾驶视觉系统开发者参考
本文探索了使用全景鱼眼相机系统实现端到端3D目标检测的技术可行性。首先研究了将传统针孔相机检测器直接迁移至鱼眼图像导致的性能下降问题。为缓解此问题，提出了两种融合鱼眼图像独特几何特性的方法：基于鸟瞰图（BEV）范式的FisheyeBEVDet，以及基于查询机制的FisheyePETR。两者均采用球面空间表示以有效捕捉鱼眼畸变。由于缺乏专用评估基准，本文发布了基于CARLA合成的Fisheye3DOD公开数据集，包含标准针孔与鱼眼相机阵列。在该数据集上的实验表明，所提鱼眼兼容建模方法相较基线模型最高提升6.2%检测精度。
原文摘要 · Abstract (English)
In this work, we explore the technical feasibility of implementing end-to-end 3D object detection (3DOD) with surround-view fisheye camera system. Specifically, we first investigate the performance drop incurred when transferring classic pinhole-based 3D object detectors to fisheye imagery. To mitigate this, we then develop two methods that incorporate the unique geometry of fisheye images into mainstream detection frameworks: one based on the bird's-eye-view (BEV) paradigm, named FisheyeBEVDet, and the other on the query-based paradigm, named FisheyePETR. Both methods adopt spherical spatial representations to effectively capture fisheye geometry. In light of the lack of dedicated evaluation benchmarks, we release Fisheye3DOD, a new open dataset synthesized using CARLA and featuring both standard pinhole and fisheye camera arrays. Experiments on Fisheye3DOD show that our fisheye-compatible modeling improves accuracy by up to 6.2% over baseline methods.
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