The KITTI Vision Benchmark Suite

We propose a deep fine-grained multi-level fusion
architecture for monocular 3D object detection,
with an additionally designed anti-occlusion
optimization process. We integrate the monocular
3D features with the pseudo-LiDAR filter
generation network between fine-grained multi-
level layers. Our network utilizes the inherent
multi-scale and promotes depth and semantic
information flow in different stages. The new
architecture can obtain features that incorporate
more reliable depth information. We propose a
novel loss function that aims at alleviating the
problem of occlusion.

We pad and resize the original image to 1720 ×
512 for training and testing. We run stochastic
gradient descent (SGD) optimizer with a momentum
0.9 and a weight decay 0.0005. The iteration
number for the training process is set to 40,000
using the “poly” learning rate policy, the base
learning rate to 0.01, and power to 0.9.

@article{liu2022fine,
title={Fine-grained Multi-level Fusion for Anti-
occlusion Monocular 3D Object Detection},
author={Liu, He and Liu, Huaping and Wang, Yikai
and Sun, Fuchun and Huang, Wenbing},
journal={IEEE Transactions on Image Processing},
year={2022},
publisher={IEEE}
}