AnyView
This is the pytorch implementation for the paper: *Anyview: Generalizable Indoor 3D Object Detection with Variable Frames*
Install / Use
/learn @xuxw98/AnyViewREADME
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
