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DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception

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IROS, 2023
Yunze Man · Liang-Yan Gui · Yu-Xiong Wang ·

Paper PDF Project Page Youtube Video Youtube Video


This repository contains a pytorch implementation for the paper: DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception. This work results in the first open analysis of cross-domain cross-sensor perception and adaptation for monocular 3D tasks in the wild.

Overview

overview

Preparation

Clone this repository:

git clone https://github.com/YunzeMan/DualCross
cd DualCross

Install requirements in a virtual environment (Tested on anaconda/miniconda environment):

conda create --name dualcross --file requirements.txt

Download nuscenes data from https://www.nuscenes.org/. Install dependencies.

pip install nuscenes-devkit tensorboardX efficientnet_pytorch==0.7.0

The code is tested on Python 3.6.10 with Pytorch 1.5.0+cu102 and 1.7.0+cu110.

Train a Model

To train the LiDAR-Teacher model first

python main.py train  --logdir=./runs-lteacher  --dataroot=/directory/to/dataset  --bsz=8  --gpuid=0  --domain_gap=True  --source='day'  --target='night'  --parser_name='lidarinputdata'  --up_scale=4  --color_jitter=True  --rand_resize=True  --teacher_student=True  --adv_training=False

To train the Image-Student model

python main.py train  --logdir=./runs-istudent  --dataroot=/directory/to/dataset  --bsz=4  --gpuid=0  --domain_gap=True  --source='day'  --target='night'  --parser_name='imglidardata'  --up_scale=4  --color_jitter=True --rand_resize=True  --adv_training=True  --align_place='midfinal'  --domainloss_lambda=0.1  --middomain_weight=0.1  --teacher_student=True  --train_student=True  --modelf_teacher=/directory/to/trained/teacher/model  --teacher_lambda=1.0  --use_gt=True  --gt_lambda=0.1  --use_depth=True  --depth_lambda=0.05

Visualize Results

python main.py viz_model_preds --domain_gap=True --source='day' --target='night' --gpuid=1  --nsweeps=1  --up_scale=4  --bsz=1  --version='trainval'  --modelf=/directory/to/trained/model  --parser_name='vizdata'  --viz_gt=False  --viz_depth=True  --mask_2d=True --sep_dpft=False  --strict_import=True  --adv_training=True  --align_midfin=True

Make Videos

python main.py make_video  --vis_folder='./visualization'  --vtype='tags'  --start_frame=0 --end_frame=150

A More Detailed Doc is Coming Soon

Demo

A comparison between Cross-domain Adaptation (Left) and DualCross (Right) on day -> night scenario.

Citation

If you find our work useful in your research, please cite:

@inproceedings{man2023dualcross,
  title={{DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception}},
  author={Man, Yunze and Gui, Liang-Yan and Wang, Yu-Xiong},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2023}
}

Acknowledgements

This repo is built based on the fantastic work Lift-Splat-Shoot by Jonah Philion and Sanja Fidler.