DaF-BEVSeg：基于畸变感知鱼眼相机并具备遮挡推理的鸟瞰视图分割
DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning
AI总结：
针对环视鱼眼相机缺少BEV分割数据集与去畸变缺陷的问题，本文构建Cognata合成数据集，提出支持任意相机模型、带畸变感知BEV池化和遮挡推理的DaF-BEVSeg。
摘要：
语义分割是执行场景理解的一种有效方式。近年来，三维鸟瞰视图（BEV）空间中的分割因其可被驾驶策略直接使用而受到欢迎。然而，针对环视鱼眼相机的BEV分割研究仍然有限，而这类相机普遍用于商用车辆。由于该任务没有真实世界的公开数据集，并且现有合成数据集未处理由遮挡造成的非模态区域，我们使用Cognata模拟器创建了一个合成数据集，其中包含多样的道路类型、天气和光照条件。我们将BEV分割泛化，使其能够适用于任意相机模型；这对于混合使用多种相机非常有用。我们通过对鱼眼图像施加柱面校正并使用标准的基于LSS的BEV分割模型实现了一个基线。我们证明，无需去畸变即可获得更好的性能，而去畸变会带来因预处理导致运行时间增加、视场减小以及重采样伪影等不利影响。此外，我们提出了一种对鱼眼相机更有效的畸变感知可学习BEV池化策略。我们用一个遮挡推理模块扩展了该模型，这对于在BEV空间中进行估计至关重要。DaF-BEVSeg的定性性能在视频https://streamable.com/ge4v51中展示。
英文摘要：
Semantic segmentation is an effective way to perform scene understanding. Recently, segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by drive policy. However, there is limited work on BEV segmentation for surround-view fisheye cameras, commonly used in commercial vehicles. As this task has no real-world public dataset and existing synthetic datasets do not handle amodal regions due to occlusion, we create a synthetic dataset using the Cognata simulator comprising diverse road types, weather, and lighting conditions. We generalize the BEV segmentation to work with any camera model; this is useful for mixing diverse cameras. We implement a baseline by applying cylindrical rectification on the fisheye images and using a standard LSS-based BEV segmentation model. We demonstrate that we can achieve better performance without undistortion, which has the adverse effects of increased runtime due to pre-processing, reduced field-of-view, and resampling artifacts. Further, we introduce a distortion-aware learnable BEV pooling strategy that is more effective for the fisheye cameras. We extend the model with an occlusion reasoning module, which is critical for estimating in BEV space. Qualitative performance of DaF-BEVSeg is showcased in the video at https://streamable.com/ge4v51.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
