Tracking evaluation for SemanticKITTI

This repo contains code for the paper 4D Panoptic Lidar Segmentation. The code is based on the Pytoch implementation of KPConv.

Installation

git clone https://github.com/MehmetAygun/4D-PLS
cd 4D-PLS
pip install -r requirements.txt
cd cpp_wrappers
sh compile_wrappers.sh

Data

Create a directory data in main directory, download the SemanticKitti to there with labels from here

Also add semantic semantic-kitti.yaml file in SemanticKitti folder, you can download the file from here

Then create additional labels using utils/create_center_label.py,

python create_center_label.py

The data folder structure should be as follows:

data/SemanticKitti/
└── semantic-kitti.yaml
└── sequences/
    └── 08/
        └── poses.txt
        └── calib.txt
        └── times.txt
        └── labels
            ├── 000000.label
            ...
         └── velodyne
            ├── 000000.bin
            ...

Models

For saving models or using pretrained models create a folder named results in main directory. You can download a pre-trained model from here .

Training

For training, you should modify the config parameters in train_SemanticKitti.py. The most important thing that, to get a good performance train the model using config.pre_train = True firstly at least for 200 epochs, then train the model using config.pre_train = False.

python train_SemanticKitti.py

This code will generate config file and save the pre-trained models in the results directory.

Testing & Tracking

For testing, set the model directory the choosen_log in test_models.py, and modify the config parameters as you wish. Then run :

python test_models.py

This will generate semantic and instance predictions for small 4D volumes under the test/model_dir. To generate long tracks using small 4D volumes use stitch_tracklets.py

python stitch_tracklets.py --predictions test/model_dir --n_test_frames 4

This code will generate predictions in the format of SemanticKITTI under test/model_dir/stitch .

Evaluation

For getting the metrics introduced in the paper, use utils/evaluate_4dpanoptic.py

python evaluate_4dpanoptic.py --dataset=SemanticKITTI_dir --predictions=output_of_stitch_tracket_dir --data_cfg=semantic-kitti.yaml