In this paper, we propose a novel and effective Multi-Level Fusion network,
named as MLF-DET, for high-performance cross-modal 3D object DETection, which
integrates both the feature-level fusion and decision-level fusion to fully
utilize the information in the image. For the feature-level fusion, we present
the Multi-scale Voxel Image fusion (MVI) module, which densely aligns
multi-scale voxel features with image features. For the decision-level fusion,
we propose the lightweight Feature-cued Confidence Rectification (FCR) module
which further exploits image semantics to rectify the confidence of detection
candidates. Besides, we design an effective data augmentation strategy termed
Occlusion-aware GT Sampling (OGS) to reserve more sampled objects in the
training scenes, so as to reduce overfitting. Extensive experiments on the
KITTI dataset demonstrate the effectiveness of our method. Notably, on the
extremely competitive KITTI car 3D object detection benchmark, our method
reaches 82.89% moderate AP and achieves state-of-the-art performance without
bells and whistles.

本文提出了一种名为 MLF-DET 的新颖有效的多级融合网络，用于高性能跨模态 3D 物体检测，它将特征级融合和决策级融合相结合，充分利用图像中的信息。