  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.
