  To achieve reliable and precise scene understanding, autonomous vehicles
typically incorporate multiple sensing modalities to capitalize on their
complementary attributes. However, existing cross-modal 3D detectors do not
fully utilize the image domain information to address the bottleneck issues of
the LiDAR-based detectors. This paper presents a new cross-modal 3D object
detector, namely UPIDet, which aims to unleash the potential of the image
branch from two aspects. First, UPIDet introduces a new 2D auxiliary task
called normalized local coordinate map estimation. This approach enables the
learning of local spatial-aware features from the image modality to supplement
sparse point clouds. Second, we discover that the representational capability
of the point cloud backbone can be enhanced through the gradients
backpropagated from the training objectives of the image branch, utilizing a
succinct and effective point-to-pixel module. Extensive experiments and
ablation studies validate the effectiveness of our method. Notably, we achieved
the top rank in the highly competitive cyclist class of the KITTI benchmark at
the time of submission. The source code is available at
https://github.com/Eaphan/UPIDet.
