DmifNet
Citing this work
If you find our code or paper useful, please consider citing
@inproceedings{li2021dmifnet,
title={DmifNet: 3D Shape Reconstruction based on Dynamic Multi-Branch Information Fusion},
author={Li, Lei and Wu, Suping},
booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
pages={7219--7225},
year={2021},
organization={IEEE}
}
Configuration Environment
Python3.6, Pytorch: 1.0, CUDA: 9.0+, Cudnn: follow Cuda version, GPU: one Nvidia RTX 2080Ti
Epoch: Approximately close to the optimal convergence value between 1500 and 2500 epoch
Installation
First you have to make sure that you have all dependencies in place.
You can create an anaconda environment called dmifnet_space
using
conda env create -f dmif_env.yaml
conda activate dmifnet_space
Then, compile the extension modules.
python set_env_up.py build_ext --inplace
Then, download the BatchNet modules.
Next, put your model path in DmifNet/dmifnet/encoder/batchnet.py /def resnet18(pretrained=False, **kwargs):
Generation
To generate meshes using a trained model, use
python generate.py ./config/img/dmifnet.yaml
Training
python train.py ./config/img/dmifnet.yaml
DataSet
You can check the baseline work Onet to download the datasetONet and DmifNet: DataSet. Thanks for contribution of baseline work.
Evaluation
First, to generate meshes using a trained model, use
python generate.py ./config/img/dmifnet.yaml
Then, for evaluation of the models, you can run it using
python eval_meshes.py ./config/img/dmifnet.yaml
also can use quick evaluation(don't need generation).
python eval.py ./config/img/dmifnet.yaml
Pretrained model
you can download our pretrained model via Baidu Netdisk or Google Drive
Quantitative Results
Method | Intersection over Union | Normal consistency | Chamfer distance |
---|---|---|---|
3D-R2N2 | 0.493 | 0.695 | 0.278 |
Pix2Mesh | 0.480 | 0.772 | 0.216 |
AtlasNet | -- | 0.811 | 0.175 |
ONet | 0.571 | 0.834 | 0.215 |
DmifNet | 0.607 | 0.846 | 0.185 |
Qualitative Results
Futher Information
If you have any problems with the code, please list the problems you encountered in the issue area, and I will reply you soon. Thanks for baseline work Occupancy Networks - Learning 3D Reconstruction in Function Space.