We provide two ways to install the dependencies of EFM3D. We recommend using miniconda to manage the dependencies, which
also provide a easy setup to for all the additional dependencies listed in requirements.txt and requirements-extra.txt.
First install miniconda,
then run the following commands under the <EFM3D_DIR> root directory
conda env create --file=environment.yml
conda activate efm3d
cd efm3d/thirdparty/mmdetection3d/cuda/
python setup.py install
The commands will first create a conda environment named efm3d, and then build the
third-party CUDA kernel required for training.
Make sure you have
Python>=3.9, then install the dependencies using pip.
The packages in requirements.txt are needed for the basic functionalities of
EFM3D, such as running the example model inference to see 3D object detection
and surface reconstruction on a vrs
sequence.
pip install -r requirements.txt
Additional dependencies in requirements-extra.txt are needed for training and eval.
pip install -r requirements-extra.txt
Important: For training, we also need to built a CUDA kernel from mmdetection3d. Compile the CUDA kernel of the IoU3d loss by running the following commands, which requires the installation of CUDA dev toolkit.
cd efm3d/thirdparty/mmdetection3d/cuda/
python setup.py install