  Recently, fusing the LiDAR point cloud and camera image to improve the
performance and robustness of 3D object detection has received more and more
attention, as these two modalities naturally possess strong complementarity. In
this paper, we propose EPNet++ for multi-modal 3D object detection by
introducing a novel Cascade Bi-directional Fusion~(CB-Fusion) module and a
Multi-Modal Consistency~(MC) loss. More concretely, the proposed CB-Fusion
module enhances point features with plentiful semantic information absorbed
from the image features in a cascade bi-directional interaction fusion manner,
leading to more powerful and discriminative feature representations. The MC
loss explicitly guarantees the consistency between predicted scores from two
modalities to obtain more comprehensive and reliable confidence scores. The
experimental results on the KITTI, JRDB and SUN-RGBD datasets demonstrate the
superiority of EPNet++ over the state-of-the-art methods. Besides, we emphasize
a critical but easily overlooked problem, which is to explore the performance
and robustness of a 3D detector in a sparser scene. Extensive experiments
present that EPNet++ outperforms the existing SOTA methods with remarkable
margins in highly sparse point cloud cases, which might be an available
direction to reduce the expensive cost of LiDAR sensors. Code is available at:
https://github.com/happinesslz/EPNetV2.
