Source code for the paper LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction.
Run ./install_dependencies.sh to install the dependencies.
You may want to create a virtual environment before you do so.
ShapeNet dataset is used in the experiments. It consists of two parts - rendered images and ground truth voxel grids. You can access the dataset as follows,
- Rendered images: http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz
- GT voxel grids: http://cvgl.stanford.edu/data2/ShapeNetVox32.tgz
Please download and save them in any location you prefer. Don't forget to adjust the dataset path in the config file.
Pre-trained multi-view and single-view models can be found here.
Please download and place them under the checkpoints directory.
The eval.py can be used to evaluate the models on the ShapeNet dataset. The script takes three inputs -
path to the config file, path to the checkpoint and number of views.
Following command will evaluate the multi-view model on 4 input views from ShapeNet:
python eval.py legoformer/config/legoformer_m.yaml checkpoints/legoformer_m.ckpt 4Similar command to evaluate the single-view model will be:
python eval.py legoformer/config/legoformer_s.yaml checkpoints/legoformer_s.ckpt 1@misc{yagubbayli2021legoformer,
title={LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction},
author={Farid Yagubbayli and Alessio Tonioni and Federico Tombari},
year={2021},
eprint={2106.12102},
archivePrefix={arXiv},
primaryClass={cs.CV}
}