This is the pytorch implementation for the paper: Revisiting Indoor 3D Object Detection: A New Practical Setting and Framework, which is in submission to RAL.

Demo

To be uploaded.

Installation

Our code is tested with PyTorch 1.9.0, CUDA 10.2 and Python 3.6. It may work with other versions.

You will need to install pointnet2 layers by running

cd third_party/pointnet2 && python setup.py install

You will also need Python dependencies (either conda install or pip install)

matplotlib

opencv-python

plyfile

'trimesh>=2.35.39,<2.35.40'

'networkx>=2.2,<2.3'

scipy

imageio

scikit-image

opencv

numpy

Dataset preparation

The instructions for preprocessing ScanNet are here.

Training

Run the following command:

python main.py --dataset_name scannetAV --model_name 3detr_sepview --checkpoint_dir <path to store outputs> --num_views <number of frames>

Testing

Run the following command:

python main.py --dataset_name scannetAV --model_name 3detr_sepview --test_ckpt <path_to_checkpoint> --test_only