Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving
for its powerful spatial representation ability. It is challenging to estimate
the BEV semantic maps from monocular images due to the spatial gap, since it is
implicitly required to realize both the perspective-to-BEV transformation and
segmentation. We present a novel two-stage Geometry Prior-based Transformation
framework named GitNet, consisting of (i) the geometry-guided pre-alignment and
(ii) ray-based transformer. In the first stage, we decouple the BEV
segmentation into the perspective image segmentation and geometric prior-based
mapping, with explicit supervision by projecting the BEV semantic labels onto
the image plane to learn visibility-aware features and learnable geometry to
translate into BEV space. Second, the pre-aligned coarse BEV features are
further deformed by ray-based transformers to take visibility knowledge into
account. GitNet achieves the leading performance on the challenging nuScenes
and Argoverse Datasets.

本文介绍了一种名为 GitNet 的新型两阶段几何先验变换框架，以解决从单眼图像估计 BEV（鸟瞰图）语义地图的挑战，从而为自动驾驶提供强大的空间表示能力。GitNet 通过先进行几何引导的预对齐，然后再运用基于射线的变换模型进行分割来分别对 BEV 分割进行处理，具有较好的性能表现。