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.

通过显式地在鸟瞰视角空间中建模高度的方式，我们提出了 HeightFormer 方法，该方法可以准确地估计鸟瞰视角中的高度，而无需使用额外数据。基准结果显示，与仅使用相机的方法相比，HeightFormer 的性能达到了最先进水平。

HeightFormer: 鸟瞰视角的摄像头仅使用，无需额外数据的显性高度建模的 3D 物体检测

HeightFormer: Explicit Height Modeling without Extra Data for  Camera-only 3D Object Detection in Bird's Eye View

Bird's eye view (BEV) representation is a new perception formulation for
autonomous driving, which is based on spatial fusion. Further, temporal fusion
is also introduced in BEV representation and gains great success. In this work,
we propose a new method that unifies both spatial and temporal fusion and
merges them into a unified mathematical formulation. The unified fusion could
not only provide a new perspective on BEV fusion but also brings new
capabilities. With the proposed unified spatial-temporal fusion, our method
could support long-range fusion, which is hard to achieve in conventional BEV
methods. Moreover, the BEV fusion in our work is temporal-adaptive and the
weights of temporal fusion are learnable. In contrast, conventional methods
mainly use fixed and equal weights for temporal fusion. Besides, the proposed
unified fusion could avoid information lost in conventional BEV fusion methods
and make full use of features. Extensive experiments and ablation studies on
the NuScenes dataset show the effectiveness of the proposed method and our
method gains the state-of-the-art performance in the map segmentation task.

本文提出一种新的空时融合 BEV 表征方法，可支持长距离融合，具有时变自适应权重，有效避免传统方法中的信息丢失和充分利用功能，同时实现地图分割任务的最优性能。