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https://github.com/galmetzer/self-sample
Single shape Deep Point Cloud Consolidation [TOG 2021]
https://github.com/galmetzer/self-sample
deep-learning point-cloud pytorch
Last synced: about 2 months ago
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Single shape Deep Point Cloud Consolidation [TOG 2021]
- Host: GitHub
- URL: https://github.com/galmetzer/self-sample
- Owner: galmetzer
- License: mit
- Created: 2021-06-02T15:25:21.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-05-21T07:04:57.000Z (over 2 years ago)
- Last Synced: 2024-07-30T19:57:07.326Z (6 months ago)
- Topics: deep-learning, point-cloud, pytorch
- Language: Python
- Homepage:
- Size: 28.3 MB
- Stars: 43
- Watchers: 2
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Self-Sampling - for neural point cloud consolidation
We introduce a novel technique for neural point cloud consolidation
which learns from only the input point cloud.
### TOG 2021 [[Paper]](https://arxiv.org/abs/2008.06471) [[Project Page]](https://galmetzer.github.io/self-sample/)
by [Gal Metzer](https://galmetzer.github.io/), [Rana Hanocka](https://www.cs.tau.ac.il/~hanocka/), [Raja Giryes](http://web.eng.tau.ac.il/~raja), and [Daniel Cohen-Or](https://danielcohenor.com/)
# Getting Started
### Installation
- Clone this repo:
#### Setup Conda Environment
- Relies on [PyTorch](https://pytorch.org/) version 1.7.1
- [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric)
- Everything can be installed via conda environment `conda env create -f env.yml` (creates an environment called self-sample)
# Running Examples
The demos folder contains examples from the paper.
For each shape the demo runs the optimization and inference parts.
For instance, to run the lamp demo simply execute from the root project folder:
```
demos/lamp.sh
```
The results would be found at `demos-results/lamp/lamp_result.xyz`,
and respectively for the other shapes as well.
#### Example shapes
- alien, anchor, lamp - sharp point consolidation
- candle, scanned Leg, tiki - sparse point consolidation
- camera_noised - denoising
# Citation
If you find this code useful, please consider citing our paper
```
@article{metzer2020self,
author = {Metzer, Gal and Hanocka, Rana and Giryes, Raja and Cohen-Or, Daniel},
title = {Self-Sampling for Neural Point Cloud Consolidation},
year = {2021},
issue_date = {October 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {40},
number = {5},
issn = {0730-0301},
url = {https://doi.org/10.1145/3470645},
doi = {10.1145/3470645},
}
```
# Questions / Issues
If you have questions or issues running this code, please open an issue.
Note: the original implementation used [this implementation](https://github.com/erikwijmans/Pointnet2_PyTorch)
of PointNet++, which is not guaranteed to supported newer versions of pytorch.
This implementation uses [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric) instead,
which can not hold large subsets at train time.
Therefore, demos are designed for subset sizes lower than used in the paper.
Increasing the subset size to 12K-14K on an appropriate GPU, improves the accuracy of the results.