Abstract
Transforming image features from perspective view (PV) space to bird's-eye-view (BEV) space remains challenging in autonomous driving due to depth ambiguity and occlusion. Although several view transformation (VT) paradigms have been proposed, the challenge still remains. In this paper, we propose a new regularization framework, dubbed CycleBEV, that enhances existing VT models for BEV semantic segmentation. Inspired by cycle consistency, widely used in image distribution modeling, we devise an inverse view transformation (IVT) network that maps BEV segmentation maps back to PV segmentation maps and use it to regularize VT networks during training through cycle consistency losses, enabling them to capture richer semantic and geometric information from input PV images. To further exploit the capacity of the IVT network, we introduce two novel ideas that extend cycle consistency into geometric and representation spaces. We evaluate CycleBEV on four representative VT models covering three major paradigms using the large-scale nuScenes dataset. Experimental results show consistent improvements -- with gains of up to 0.74, 4.86, and 3.74 mIoU for drivable area, vehicle, and pedestrian classes, respectively -- without increasing inference complexity, since the IVT network is used only during training. The implementation code is available at this https URL.
Abstract (translated)
将图像特征从透视视图（PV）空间转换到鸟瞰视图（BEV）空间，在自动驾驶中仍然是一个挑战，主要是由于深度模糊和遮挡问题。尽管已经提出了几种视角变换（VT）范式，但这一难题依然存在。在本文中，我们提出了一种新的正则化框架，称为CycleBEV，该框架能够增强现有的用于鸟瞰视图语义分割的视角变换模型。 受到循环一致性概念的启发，这种概念广泛应用于图像分布建模领域，我们设计了一个逆向视角变换（IVT）网络，它将鸟瞰视图分割映射转换回透视视图分割映射，并利用该网络在训练过程中通过循环一致性损失来正则化VT模型。这种方法使它们能够从输入的PV图像中捕捉到更丰富的语义和几何信息。 为了进一步挖掘IVT网络的能力，我们引入了两个新颖的想法，这些想法将循环一致性扩展到了几何空间和表示空间。我们在nuScenes大规模数据集上对CycleBEV进行了评估，涵盖了四个具有代表性的VT模型以及三大主要范式。 实验结果表明，在不增加推理复杂度的情况下（由于IVT网络仅在训练期间使用），实现了显著改进——对于可驾驶区域、车辆和行人等类别的平均交并比(mIoU)分别提高了0.74、4.86和3.74。这些结果证明了CycleBEV的有效性。 实现代码可在提供的链接处获取（原文中的链接请自行查找）。
URL
https://arxiv.org/abs/2602.23575
