  Besides standard cameras, autonomous vehicles typically include multiple
additional sensors, such as lidars and radars, which help acquire richer
information for perceiving the content of the driving scene. While several
recent works focus on fusing certain pairs of sensors - such as camera with
lidar or radar - by using architectural components specific to the examined
setting, a generic and modular sensor fusion architecture is missing from the
literature. In this work, we propose HRFuser, a modular architecture for
multi-modal 2D object detection. It fuses multiple sensors in a
multi-resolution fashion and scales to an arbitrary number of input modalities.
The design of HRFuser is based on state-of-the-art high-resolution networks for
image-only dense prediction and incorporates a novel multi-window
cross-attention block as the means to perform fusion of multiple modalities at
multiple resolutions. We demonstrate via extensive experiments on nuScenes and
the adverse conditions DENSE datasets that our model effectively leverages
complementary features from additional modalities, substantially improving upon
camera-only performance and consistently outperforming state-of-the-art 3D and
2D fusion methods evaluated on 2D object detection metrics. The source code is
publicly available.
