  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.
