The KITTI Vision Benchmark Suite

We migrate the 2D object detection data augmentation
method to 3D object detection without losing
coordinate cues and size cues, and experimentally
prove that our migration method is necessary. The
sampling bias problem of data augmentation is
explored in this process, and an unbiased sampling
strategy and several new augmentation methods for 3D
object detection are proposed, and the effectiveness
of the proposed methods is experimentally verified.

lr=1e-4

@ARTICLE{10497146,
author={Jia, Yisong and Wang, Jue and Pan,
Huihui and Sun, Weichao},
journal={IEEE Transactions on Instrumentation
and Measurement},
title={Enhancing Monocular 3-D Object Detection
Through Data Augmentation Strategies},
year={2024},
volume={73},
number={},
pages={1-11},
keywords={Three-dimensional displays;Object
detection;Data augmentation;Task
analysis;Pipelines;Cameras;Detectors;Autonomous
driving;data augmentation;deep learning;monocular
3-D object detection},
doi={10.1109/TIM.2024.3387500}}