PyTorch(1.9.0) training, evaluating and pretrained models for CLGo (Learning to Predict 3D Lane Shape and Camera Pose with Geometry Constraints).
- Predicting 3D lanes and camera pose from a single image.
- Learning via geometry constraints to improve performances on both tasks.
We provide the CLGo model files in the .CLGoZoos/.
- Linux ubuntu 16.04
- GeForce RTX 3090
- Python 3.8.5
- CUDA 11.1
Create virtualenv environment
python3 -m venv CLGOENV
Activate it
source CLGOENV/bin/activate
Then install dependencies
pip install torch==1.9.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
Download and extract ApolloSim from yuliangguo/3D_Lane_Synthetic_Dataset
We expect the directory structure to be the following:
./CLGOENV
./CLGoZoos
./Apollo_Sim_3D_Lane_Release
(1) Balanced scenes
python joint_train.py IMG_Seq_Pv-Tv_standard
(2) Rarely observed scenes
python joint_train.py IMG_Seq_Pv-Tv_rare_subset
(3) Scenes with visual variations
python joint_train.py IMG_Seq_Pv-Tv_illus_chg
(1) Balanced scenes
python fast_joint_test.py IMG_Seq_Pv-Tv_standard --test_mode PvTv
(2) Rarely observed scenes
python fast_joint_test.py IMG_Seq_Pv-Tv_rare_subset --test_mode PvTv
(3) Scenes with visual variations
python fast_joint_test.py IMG_Seq_Pv-Tv_illus_chg --test_mode PvTv