  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.
