The KITTI Vision Benchmark Suite

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.
\zliu{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.

None

@ARTICLE{9983516, author={Liu, Zhe and Huang,
Tengteng and Li, Bingling and Chen, Xiwu and Wang,
Xi and Bai, Xiang}, journal={IEEE Transactions on
Pattern Analysis and Machine Intelligence},
title={EPNet++: Cascade Bi-Directional Fusion for
Multi-Modal 3D Object Detection}, year={2022},
volume={}, number={}, pages={1-18}, doi=
{10.1109/TPAMI.2022.3228806}}