  Vision-based Bird's Eye View (BEV) representation is an emerging perception
formulation for autonomous driving. The core challenge is to construct BEV
space with multi-camera features, which is a one-to-many ill-posed problem.
Diving into all previous BEV representation generation methods, we found that
most of them fall into two types: modeling depths in image views or modeling
heights in the BEV space, mostly in an implicit way. In this work, we propose
to explicitly model heights in the BEV space, which needs no extra data like
LiDAR and can fit arbitrary camera rigs and types compared to modeling depths.
Theoretically, we give proof of the equivalence between height-based methods
and depth-based methods. Considering the equivalence and some advantages of
modeling heights, we propose HeightFormer, which models heights and
uncertainties in a self-recursive way. Without any extra data, the proposed
HeightFormer could estimate heights in BEV accurately. Benchmark results show
that the performance of HeightFormer achieves SOTA compared with those
camera-only methods.
