The combination of LiDAR and camera modalities is proven to be necessary and
typical for 3D object detection according to recent studies. Existing fusion
strategies tend to overly rely on the LiDAR modal in essence, which exploits
the abundant semantics from the camera sensor insufficiently. However, existing
methods cannot rely on information from other modalities because the corruption
of LiDAR features results in a large domain gap. Following this, we propose
CrossFusion, a more robust and noise-resistant scheme that makes full use of
the camera and LiDAR features with the designed cross-modal complementation
strategy. Extensive experiments we conducted show that our method not only
outperforms the state-of-the-art methods under the setting without introducing
an extra depth estimation network but also demonstrates our model's noise
resistance without re-training for the specific malfunction scenarios by
increasing 5.2\% mAP and 2.4\% NDS.

本研究提出一种更健壮和噪声抗性的跨模态融合策略 - CrossFusion，充分利用设计的跨模式补充策略的相机和 LiDAR 特征，实验表明我们的方法不仅在不引入额外深度估计网络的情况下优于现有方法，还证明我们的模型具有噪声抗性和不需要重新训练，能够应用于特定故障场景，增加了 5.2％的平均精度（mAP）和 2.4％的归一化检测得分（NDS）