The KITTI Vision Benchmark Suite

We contend that many components of monocular 3D
object detection lack the necessary adaptability,
impeding the performance of the detector. In this
paper, we propose six adaptive methods addressing
issues related to network structure, loss
function, and optimizer. These methods
specifically target the rigid components within
the detector that hinder adaptability.
Simultaneously, we provide theoretical insights
into the network output and propose two novel
regression methods. These methods facilitate more
straightforward learning for the network.

DDAD15M pretrain and flip test

@ARTICLE{10843993,
author={Pan, Huihui and Jia, Yisong and Wang,
Jue and Sun, Weichao},
journal={IEEE Transactions on Intelligent
Transportation Systems},
title={MonoAMNet: Three-Stage Real-Time
Monocular 3D Object Detection With Adaptive
Methods},
year={2025},
volume={},
number={},
pages={1-14},
keywords={Monocular 3D object detection;deep
learning;autonomous driving;optimizer},
doi={10.1109/TITS.2025.3525772}
}