Recent vision-only perception models for autonomous driving achieved
promising results by encoding multi-view image features into Bird's-Eye-View
(BEV) space. A critical step and the main bottleneck of these methods is
transforming image features into the BEV coordinate frame. This paper focuses
on leveraging geometry information, such as depth, to model such feature
transformation. Existing works rely on non-parametric depth distribution
modeling leading to significant memory consumption, or ignore the geometry
information to address this problem. In contrast, we propose to use parametric
depth distribution modeling for feature transformation. We first lift the 2D
image features to the 3D space defined for the ego vehicle via a predicted
parametric depth distribution for each pixel in each view. Then, we aggregate
the 3D feature volume based on the 3D space occupancy derived from depth to the
BEV frame. Finally, we use the transformed features for downstream tasks such
as object detection and semantic segmentation. Existing semantic segmentation
methods do also suffer from an hallucination problem as they do not take
visibility information into account. This hallucination can be particularly
problematic for subsequent modules such as control and planning. To mitigate
the issue, our method provides depth uncertainty and reliable visibility-aware
estimations. We further leverage our parametric depth modeling to present a
novel visibility-aware evaluation metric that, when taken into account, can
mitigate the hallucination problem. Extensive experiments on object detection
and semantic segmentation on the nuScenes datasets demonstrate that our method
outperforms existing methods on both tasks.

本文提出了一种利用几何信息通过参数化深度分布建模来进行图像特征转换的方法，通过将 2D 图像特征提升到自车定义的三维空间，并根据深度导出的三维空间占用情况将三维特征体积聚合到鸟瞰视图中进行特征转换，以解决自动驾驶中图像特征转换的瓶颈问题。进一步，在语义分割中提出了一种可靠的可见性感知评估度量，通过考虑该度量可以减轻幻觉问题，并在 nuScenes 数据集上的目标检测和语义分割实验中验证了方法的优越性。