Awesome
CLGo: Learning to Predict 3D Lane Shape and Camera Pose with Geometry Constraints
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.
Model Zoo
We provide the CLGo model files in the .CLGoZoos/.
Set Envirionment
- 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
Data Preparation
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
Training
(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
Evaluation
(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