github.com/alibaba-damo-academy/former3d @main

3D Former: Monocular Scene Reconstruction with SDF 3D Transformers

This is the official PyTorch implementation code for 3D-Former. For technical details, please refer to:

3D Former: Monocular Scene Reconstruction with SDF 3D Transformers

Weihao Yuan, Xiaodong Gu, Heng Li, Zilong Dong, Siyu Zhu

ICLR 2023

[Project Page] | [Paper]

Bibtex

If you find this code useful in your research, please cite:

@inproceedings{yuan2022former3d,
  title={3D Former: Monocular Scene Reconstruction with 3D SDF Transformers},
  author={Yuan, Weihao and Gu, Xiaodong and Li, Heng and Dong, Zilong and Zhu, Siyu},
  booktitle={Proceedings of the International Conference on Learning Representations},
  pages={},
  year={2023}
}

Contents

1. Installation
2. Datasets
3. Training
4. Evaluation
5. Model

Installation

conda create -n former3d python=3.8
conda activate former3d

# Attention: The version of torchvision should be the same in training and inference. Mismatched version may generate strange results.
conda install pytorch-gpu=1.10.0 torchvision=0.9 cudatoolkit=11.1

pip install \
  pytorch-lightning==1.5 spconv-cu111==2.1.21 setuptools==59.5.0 scikit-image==0.18\
  numba pillow wandb tqdm pyyaml matplotlib black imageio easydict numpy_indexed \
  opencv-python open3d pyrender ray trimesh pycuda

pip install -e .

Datasets

You can prepare the ScanNet data according to here, or download from scannet and tsdf. Then modify the data path in the config files to your dataset locations.

Training

python scripts/train.py --config config_localtest.yml --gpus 4

Evaluation

First inference the meshes by

python scripts/inference.py --ckpt model.ckpt --outputdir results/test --split test --config config.yml --n-imgs 200 --cropsize 64

If the memory is not enough, you can reduce n-imgs with a small performance decrease.

Then evaluate the generated meshes by

MESA_GL_VERSION_OVERRIDE=4.1 python scripts/evaluate.py --results_dir results/test --split test --config config.yml --n_proc 0

Also, you can evaluate the meshes following TransformerFusion to avoid penalizing more complete reconstruction.

Model

The reconstructed meshes used for evaluation is available at here.

Acknowledgements

Thanks to Stier Noah for opening source of the excellent work VoRTX. Thanks to Jiageng Mao for opening source of the excellent work VOTR.