We recommend you to use conda. create a conda environment and install the following packages. Just use the following command line.

conda create --name avod python=3.5 && conda activate avod && conda install matplotlib -y && conda install numpy -y && conda install -c conda-forge opencv -y && conda install -c conda-forge pandas -y && conda install -c conda-forge pillow -y && conda install -c conda-forge scipy -y && conda install -c anaconda scikit-learn -y && conda install -c anaconda tensorflow-gpu==1.3.0 -y && conda install -c open3d-admin open3d -y

If you want to install the packages one by one, you can also refer to the following command lines.

conda create --name avod python=3.5
conda activate avod
conda install matplotlib -y
conda install numpy -y
conda install -c conda-forge opencv -y
conda install -c conda-forge pandas -y
conda install -c conda-forge pillow -y
conda install -c conda-forge scipy -y
conda install -c anaconda scikit-learn -y
conda install -c anaconda tensorflow-gpu==1.3.0 -y
conda install -c open3d-admin open3d -y

Please refer to the README file in the subdirectory avod_moe, click instruction for using the model to directly access the README.

The code for visualization is in the subdirectory ./visualization. Inside the folder, there are two more subfolder vis_3d and vis_weights. To visualize the final 3d bounding box prediction, you should use the code in vis_3d, to visualize the weights for different region proposals, you should use the code in vis_weights.

If you want to visualize the 3d bounding box prediction in the point cloud, you need to do the following steps:

1. Use the following code to run the visualization.

cd ./visualization/vis_3d
python vis_point_cloud.py -d ./path_to_data_folder -s scene_index -p prediction_folder -t objectness_threshold

you can also use the following lines for help with the arguments:

python vis_point_cloud.py --help

if you just want to see the effect without your own prediction files folder, just run the following to see the effect:

python vis_point_cloud.py

To visualize the MoE, you need to do the following steps:

1. Specify the path to weights data folder weights. How to generate weights? Please see this README.
2. Use the following code to run the visualization.

For help, run the following:

cd ./visualization/vis_weights
python feature_weighs_visualization.py -h # read the help to learn how to use it

To visualize scene 000008 the image features and bev features in pca:

python feature_weights_visualization.py -id path_to_image_folder -s 000008 -d directory_of_data -bf -if

To draw bounding boxes of scene 000008 on the image and bev:

python feature_weights_visualization.py -id path_to_image_folder -s 000008 -d directory_of_data -bb -ib