MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware
  Embeddings
Lei Yang Jiaxin Yu Xinyu Zhang Jun Li Li Wang Yi Huang Chuang Zhang Hong Wang Yiming Li
Although the majority of recent autonomous driving systems concentrate on
developing perception methods based on ego-vehicle sensors, there is an
overlooked alternative approach that involves leveraging intelligent roadside
cameras to help extend the ego-vehicle perception ability beyond the visual
range. We discover that most existing monocular 3D object detectors rely on the
ego-vehicle prior assumption that the optical axis of the camera is parallel to
the ground. However, the roadside camera is installed on a pole with a pitched
angle, which makes the existing methods not optimal for roadside scenes. In
this paper, we introduce a novel framework for Roadside Monocular 3D object
detection with ground-aware embeddings, named MonoGAE. Specifically, the ground
plane is a stable and strong prior knowledge due to the fixed installation of
cameras in roadside scenarios. In order to reduce the domain gap between the
ground geometry information and high-dimensional image features, we employ a
supervised training paradigm with a ground plane to predict high-dimensional
ground-aware embeddings. These embeddings are subsequently integrated with
image features through cross-attention mechanisms. Furthermore, to improve the
detector's robustness to the divergences in cameras' installation poses, we
replace the ground plane depth map with a novel pixel-level refined ground
plane equation map. Our approach demonstrates a substantial performance
advantage over all previous monocular 3D object detectors on widely recognized
3D detection benchmarks for roadside cameras. The code and pre-trained models
will be released soon.