Awesome
<!-- > AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds<br> > Runsong Zhu, Yuan Liu, Zhen Dong, Tengping jiang, Yuan Wang, Wenping Wang, Bisheng Yang<br> > [Project Page](https://runsong123.github.io/AdaFit/) Under construction ... -->AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds (ICCV 2021 oral)
Project Page | Arxiv | Video | Poster |
Runsong Zhu¹, Yuan Liu², Zhen Dong¹, Tengping jiang¹, Yuan Wang¹, Wenping Wang³, Bisheng Yang¹.
¹Wuhan University + ²The University of Hong Kong + ³Texas A&M University.
Requirements
we conduct the experiment in the following setting:
- Ubuntu 16.04
- CUDA 10.1
- Python v3.7
- Pytorch v1.4 & torchvision v0.5.0
- matplotlib v2.2.4
- numpy v1.17.4
- tensorboardX v1.9
- scikit-learn v0.21.3
- scipy v1.3.2
- urllib3 v1.25.8
How to use the code
Data praparation
you need to download PCPNet dataset and place it in ./data/
single-scale AdaFit (Train + Test on PCPNet):
python run_AdaFit_single_experiment_single_scale.py
Note that, the difference between single-scale verison of our AdaFit and DeepFit is the offset-learning part, which you only need to add the following code.:
# parameter
self.conv_bias = nn.Conv1d(128, 3, 1)
# train /test
...
bias = self.conv_bias(x)
bias[:,:,0] = 0
points = points + bias
...
AdaFit (Train + Test on PCPNet):
python run_AdaFit_single_experiment_multi_scale.py
Acknowledgement
The code is heavily based on DeepFit.
If you find our work useful in your research, please cite our paper. And please also cite the DeepFit paper.
@article{zhu2021adafit,
title={AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds},
author={Zhu, Runsong and Liu, Yuan and Dong, Zhen and Jiang, Tengping and Wang, Yuan and Wang, Wenping and Yang, Bisheng},
journal={arXiv preprint arXiv:2108.05836},
year={2021}
}
@article{ben2020deepfit,
title={DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares},
author={Ben-Shabat, Yizhak and Gould, Stephen},
journal={arXiv preprint arXiv:2003.10826},
year={2020}
}