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.

翻译：摘要：基于视觉的鸟瞰视角（BEV）表示是自动驾驶领域新兴的感知范式。其核心挑战在于利用多相机特征构建BEV空间，这是一个一对多的病态问题。深入分析已有的BEV表示生成方法后，我们发现大多数方法可分为两类：在图像视角中建模深度，或在BEV空间中建模高度，且多采用隐式方式。本文提出在BEV空间中显式建模高度，该方法无需如激光雷达（LiDAR）等额外数据，且相比深度建模能适配任意相机配置与类型。理论上，我们证明了基于高度的方法与基于深度的方法之间的等价性。基于这种等价性及高度建模的若干优势，我们提出HeightFormer，以自递归方式对高度与不确定性进行建模。无需任何额外数据，所提HeightFormer可精确估计BEV中的高度。基准测试结果表明，与纯相机方法相比，HeightFormer的性能达到了当前最优水平（SOTA）。