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deltaconv-1.0.2
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مشاهده بیشترتوضیحات
Implementation of DeltaConv: an anisotropic operator for geometric deep learning on point clouds (and other discretizations that will be added in the future).
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deltaconv-1.0.2 |
| نام | deltaconv |
| نسخه کتابخانه | 1.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ruben Wiersma |
| ایمیل نویسنده | rubenwiersma@gmail.com |
| آدرس صفحه اصلی | https://github.com/rubenwiersma/deltaconv |
| آدرس اینترنتی | https://pypi.org/project/deltaconv/ |
| مجوز | MIT |
# DeltaConv
[[Paper]](https://rubenwiersma.nl/assets/pdf/DeltaConv.pdf) [[Project page]](https://rubenwiersma.nl/deltaconv)
Code for the SIGGRAPH 2022 paper "[DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds](https://arxiv.org/abs/2111.08799)" by Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, and Klaus Hildebrandt.
Anisotropic convolution is a central building block of CNNs but challenging to transfer to surfaces. DeltaConv learns combinations and compositions of operators from vector calculus, which are a natural fit for curved surfaces. The result is a simple and robust anisotropic convolution operator for point clouds with state-of-the-art results.
*Top: unlike images, surfaces have no global coordinate system. Bottom: DeltaConv learns both scalar and vector features using geometric operators.*
## Contents
- [Installation](#installation)
- [Replicating the experiments](#replicating-the-experiments)
- [Tests](#tests)
- [Citation](#citations)
## Installation
1. Clone this repository:
```bash
git clone https://github.com/rubenwiersma/deltaconv.git
```
2. Create a conda environment from the `environment.yml`:
```bash
conda env create -n deltaconv -f environment.yml
```
Done!
### Manual installation
If you wish to install DeltaConv in your own environment, proceed as follows.
1. Make sure that you have installed:
- Numpy - `pip install numpy`
- [PyTorch](https://pytorch.org/get-started/locally/) - see [instructions](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
- [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) - `conda install pyg -c pyg`
2. Install DeltaConv:
```bash
pip install deltaconv
```
### Building DeltaConv for yourself
1. Make sure you clone the repository with submodules:
```bash
git clone --recurse-submodules https://github.com/rubenwiersma/deltaconv.git
```
If you have already cloned the repository without submodules, you can fix it with `git submodule update --init --recursive`.
2. Install from folder:
```bash
cd [root_folder]
pip install
```
## Replicating the experiments
See the README.md in `replication_scripts` for instructions on replicating the experiments and using the pre-trained weights (available in `experiments/pretrained_weights`).
In short, you can run bash scripts to replicate our experiments. For example, evaluating pre-trained weights on ShapeNet:
```bash
cd [root_folder]
conda activate deltaconv
bash replication_scripts/pretrained/shapenet.sh
```
You can also directly run the python files in `experiments`:
```bash
python experiments/train_shapenet.py
```
Use the `-h` or `--help` flag to find out which arguments can be passed to the training script:
```bash
python experiments/train_shapenet.py -h
```
You can keep track of the training process with tensorboard:
```bash
tensorboard logdir=experiments/runs/shapenet_all
```
### Anisotropic Diffusion
The code that was used to generate Figure 2 from the paper and Figure 2 and 3 from the supplement is a notebook in the folder `experiments/anisotropic_diffusion`.
## Data
The training scripts assume that you have a `data` folder in `experiments`. ModelNet40 and ShapeNet download the datasets from a public repository. Instructions to download the data for human body shape segmentation, SHREC, and ScanObjectNN are given in the training scripts.
## Tests
In the paper, we make statements about a number of properties of DeltaConv that are either a result of prior work or due to the implementation. We created a test suite to ensure that these properties hold for the implementation, along with unit tests for each module. For example:
- Section 3.6, 3.7: Vector MLPs are equivariant to norm-preserving transformations, or coordinate-independent (rotations, reflections)
- `test/nn/test_mlp.py`
- `test/nn/test_nonlin.py`
- Section 3.7: DeltaConv is coordinate-independent, a forward pass on a shape with one choice of bases leads to the same output and weight updates when run with different bases
- `test/nn/test_deltaconv.py`
- Introduction, section 3.2: The operators are robust to noise and outliers.
- `test/geometry/test_grad_div.py`
- Supplement, section 1: Vectors can be mapped between points with equation (15).
- `test/geometry/test_grad_div.py`
## Citations
Please cite our paper if this code contributes to an academic publication:
```bib
@Article{Wiersma2022DeltaConv,
author = {Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, Klaus Hildebrandt},
journal = {Transactions on Graphics},
title = {DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds},
year = {2022},
month = jul,
number = {4},
volume = {41},
doi = {10.1145/3528223.3530166},
publisher = {ACM},
}
```
The farthest point sampling code relies on Geometry Central:
```bib
@misc{geometrycentral,
title = {geometry-central},
author = {Nicholas Sharp and Keenan Crane and others},
note = {www.geometry-central.net},
year = {2019}
}
```
And we make use of PyG (and underlying packages) to load point clouds, compute sparse matrix products, and compute nearest neighbors:
```bib
@inproceedings{Fey/Lenssen/2019,
title={Fast Graph Representation Learning with {PyTorch Geometric}},
author={Fey, Matthias and Lenssen, Jan E.},
booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
year={2019},
}
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl deltaconv-1.0.2:
pip install deltaconv-1.0.2.whl
نصب پکیج tar.gz deltaconv-1.0.2:
pip install deltaconv-1.0.2.tar.gz