  Autonomous driving requires accurate and detailed Bird's Eye View (BEV)
semantic segmentation for decision making, which is one of the most challenging
tasks for high-level scene perception. Feature transformation from frontal view
to BEV is the pivotal technology for BEV semantic segmentation. Existing works
can be roughly classified into two categories, i.e., Camera model-Based Feature
Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). In
this paper, we empirically analyze the vital differences between CBFT and CFFT.
The former transforms features based on the flat-world assumption, which may
cause distortion of regions lying above the ground plane. The latter is limited
in the segmentation performance due to the absence of geometric priors and
time-consuming computation. In order to reap the benefits and avoid the
drawbacks of CBFT and CFFT, we propose a novel framework with a Hybrid Feature
Transformation module (HFT). Specifically, we decouple the feature maps
produced by HFT for estimating the layout of outdoor scenes in BEV.
Furthermore, we design a mutual learning scheme to augment hybrid
transformation by applying feature mimicking. Notably, extensive experiments
demonstrate that with negligible extra overhead, HFT achieves a relative
improvement of 13.3% on the Argoverse dataset and 16.8% on the KITTI 3D Object
datasets compared to the best-performing existing method. The codes are
available at https://github.com/JiayuZou2020/HFT.
