Supplementary Code for the CVPR'19 paper entitled Leveraging Shape Completion for 3D Siamese Tracking
@InProceedings{Giancola_2019_CVPR,
author = {Giancola, Silvio and Zarzar, Jesus and Ghanem, Bernard},
title = {Leveraging Shape Completion for 3D Siamese Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}Download the dataset from KITTI Tracking.
You will need to download the data for velodyne, calib and label_02.
Place the 3 folders in the same parent folder as following:
[Parent Folder]
--> [calib]
--> {0000-0020}.txt
--> [label_02]
--> {0000-0020}.txt
--> [velodyne]
--> [0000-0020] folders with velodynes .bin files
conda create -y -n ShapeCompletion3DTracking python tqdm numpy pandas shapely matplotlib pomegranate ipykernel jupyter imageio
source activate ShapeCompletion3DTracking
conda install -y pytorch=0.4.1 cuda90 -c pytorch
pip install pyquaternion
python main.py --train_model --model_name=<Name of your model> --dataset_path=<Path to KITTI Tracking folder>
python main.py --test_model --model_name=<Name of your model> --dataset_path=<Path to KITTI Tracking folder>
Run python main.py --help for a detailled description of the parameters.
OPT:
--model_name=<Name of your model>
--dataset_path=<Path to KITTI Tracking>
--lambda_completion=1e-6: balance between tracking and completion loss
--bneck_size=128: lenght of the latent vector
--GPU=1: enforce the use of GPU 1
--tiny: use a tiny set of KITTI Tracking
You can create the GIF visualization from the supplementary material running the following command:
python VisualizeTracking.py --model_name Ours --track 29 --path_KITTI <PATH_KITTI>
python VisualizeTracking.py --model_name PreTrained --track 29 --path_KITTI <PATH_KITTI>
python VisualizeTracking.py --model_name Random --track 29 --path_KITTI <PATH_KITTI>
usage: VisualizeTracking.py [-h] [--GPU GPU] [--model_name MODEL_NAME]
[--track TRACK] [--path_results PATH_RESULTS]
[--path_KITTI PATH_KITTI]
Visualize Tracking Results
optional arguments:
-h, --help show this help message and exit
--GPU GPU ID of the GPU to use (default: -1)
--model_name MODEL_NAME
model to infer (Random/Pretrain/Ours) (default: Ours)
--track TRACK track to infer (supp. mat. are 29/45/91) (default: 29)
--path_results PATH_RESULTS
path to save the results (default: ../results)
--path_KITTI PATH_KITTI
path for the KITTI dataset (default:
KITTI/tracking/training)
Tracking and Reconstruction of sample 29 from KITTI Tracking Dataset:
- using random weights
- using PreTrained weights
- using Our training
