test split

EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular Video

Installation

conda create -n EPRecon python=3.9
conda activate EPRecon

conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia

sudo apt-get install libsparsehash-dev
git clone -b v2.0.0 https://github.com/mit-han-lab/torchsparse.git
cd torchsparse
pip install tqdm
pip install .

git clone https://github.com/zhen6618/EPRecon.git
cd EPRecon

pip install -r requirements.txt
pip install sparsehash
pip install -U openmim
mim install mmcv-full

Dataset

1. Download and extract ScanNet by following the instructions provided at http://www.scan-net.org/.

python datasets/scannet/download_scannet.py

2. Generate depth, color, pose, intrinsics from .sens file (change your file path)

python datasets/scannet/reader.py

Expected directory structure of ScanNet can refer to NeuralRecon

3. Extract instance-level semantic labels (change your file path).

python datasets/scannet/batch_load_scannet_data.py

4. Label generation for panoptic reconstruction (change your file path):

# training/val split
python tools/tsdf_fusion/generate_gt.py --data_path datasets/scannet/ --save_name all_tsdf_9 --window_size 9
# test split
python tools/tsdf_fusion/generate_gt.py --test --data_path datasets/scannet/ --save_name all_tsdf_9 --window_size 9

5. Panoptic label interpolation (change your file path):

python datasets/scannet/label_interpolate.py

Training

python main.py --cfg ./config/train.yaml

Testing

python main.py --cfg ./config/test.yaml

Generate Results for Evaluation

python tools/generate_semantic_instance.py

Citation

@INPROCEEDINGS{zhou2025EPRecon,
  author={Zhou, Zhen and Ma, Yunkai and Fan, Junfeng and Zhang, Shaolin and Jing, Fengshui and Tan, Min},
  booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={EPRecon: An Efficient Framework for Real-Time Panoptic 3D Reconstruction from Monocular Video}, 
  year={2025},
  volume={},
  number={},
  pages={2026-2033},
}