The KITTI Vision Benchmark Suite

Low-cost monocular 3D object detection plays a
fundamental role in autonomous driving, whereas
its accuracy is still far from satisfactory. Our
objective is to dig into the 3D object detection
task and reformulate it as the sub-tasks of
object localization and appearance perception,
which benefits to a deep excavation of reciprocal
information underlying the entire task. We
introduce a Dynamic Feature Reflecting Network,
named DFR-Net, which contains two novel
standalone modules: (i) the Appearance-
Localization Feature Reflecting module (ALFR)
that first separates task-specific features and
then self-mutually reflects the reciprocal
features; (ii) the Dynamic Intra-Trading module
(DIT) that adaptively realigns the training
processes of various sub-tasks via a self-
learning manner. Extensive experiments on the
challenging KITTI dataset demonstrate the
effectiveness and generalization of DFR-Net. We
rank 1st among all the monocular 3D object
detectors in the KITTI test set (till March 16

TBD

@inproceedings{dfr2021,
title={
The devil is in the task: Exploiting reciprocal
appearance-localization features for monocular 3d
object detection
},
author={
Zou, Zhikang and Ye, Xiaoqing and Du, Liang and
Cheng, Xianhui and Tan, Xiao and Zhang, Li
and Feng, Jianfeng and Xue, Xiangyang and Ding,
Errui
},
booktitle={ICCV},
year={2021}
}