The KITTI Vision Benchmark Suite

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 module, which densely aligns multi-scale
voxel features with image features. For the
decision-level fusion, we propose the lightweight
Feature-cued Confidence Rectification 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 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.

TBD

@inproceedings{lin2023mlf,
title={MLF-DET: Multi-Level Fusion for Cross-
Modal 3D Object Detection},
author={Lin, Zewei and Shen, Yanqing and Zhou,
Sanping and Chen, Shitao and Zheng, Nanning},
booktitle={International Conference on
Artificial Neural Networks},
pages={136--149},
year={2023},
organization={Springer}
}