Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

Last update: Dec 30, 2022

Related tags

Deep Learning AdaFit

Overview

AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds (ICCV 2021 oral)

**Project Page | Arxiv **

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}
}

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Comments

Multi AdaFit Training： RuntimeError: inconsistent tensors dimensions input: 2 other: 3

Sorry for bothering you. When I train a multi-model, the line 383 of train_n_est_multi_scale.py throws an error train_n_est_multi_scale.py. The code is normal_loss = 0.5 * torch.norm(torch.cross(o_pred, o_target, dim=-1), p=2, dim=1).mean() It seems that the shape of o_pred([128, 3]) and o_target([128, 175, 3) is not matched. But I have no idea where to change, sorry for bothering you again. Gratefully.

opened by Lionel-McCree 3
How to visualize the error and estimated normals in Figure 7 and Figure 8 in the paper

Dear authors, Thanks for your great work. Can you share how to visualize the error and estimated normal in Figure 7 and Figure 8 in the paper. Thank you very much for your reply.

opened by liuhw923 0

Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

Related tags

Overview

AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds (ICCV 2021 oral)

Requirements

How to use the code

Data praparation

single-scale AdaFit (Train + Test on PCPNet):

AdaFit (Train + Test on PCPNet):

Acknowledgement

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How to visualize the error and estimated normals in Figure 7 and Figure 8 in the paper

Owner

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