📝 update README.md for better installation instructions
This commit is contained in:
parent
377132fdd5
commit
43164f892f
31
README.md
31
README.md
|
@ -6,9 +6,9 @@
|
|||
|
||||
This repository contains the implementation of the paper:
|
||||
|
||||
Shape As Points: A Differentiable Poisson Solver
|
||||
[Songyou Peng](https://pengsongyou.github.io/), [Chiyu "Max" Jiang](https://www.maxjiang.ml/), [Yiyi Liao](https://yiyiliao.github.io/), [Michael Niemeyer](https://m-niemeyer.github.io/), [Marc Pollefeys](https://www.inf.ethz.ch/personal/pomarc/) and [Andreas Geiger](http://www.cvlibs.net/)
|
||||
**NeurIPS 2021 (Oral)**
|
||||
Shape As Points: A Differentiable Poisson Solver
|
||||
[Songyou Peng](https://pengsongyou.github.io/), [Chiyu "Max" Jiang](https://www.maxjiang.ml/), [Yiyi Liao](https://yiyiliao.github.io/), [Michael Niemeyer](https://m-niemeyer.github.io/), [Marc Pollefeys](https://www.inf.ethz.ch/personal/pomarc/) and [Andreas Geiger](http://www.cvlibs.net/)
|
||||
**NeurIPS 2021 (Oral)**
|
||||
|
||||
|
||||
If you find our code or paper useful, please consider citing
|
||||
|
@ -23,7 +23,7 @@ If you find our code or paper useful, please consider citing
|
|||
|
||||
## Installation
|
||||
First you have to make sure that you have all dependencies in place.
|
||||
The simplest way to do so, is to use [anaconda](https://www.anaconda.com/).
|
||||
The simplest way to do so, is to use [anaconda](https://www.anaconda.com/).
|
||||
|
||||
You can create an anaconda environment called `sap` using
|
||||
```
|
||||
|
@ -31,14 +31,15 @@ conda env create -f environment.yaml
|
|||
conda activate sap
|
||||
```
|
||||
|
||||
Next, you should install [PyTorch3D](https://pytorch3d.org/) (**>=0.5**) yourself from the [official instruction](https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md#3-install-wheels-for-linux).
|
||||
Next, you should install [PyTorch3D](https://pytorch3d.org/) (**>=0.5**) yourself from the [official instruction](https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md#3-install-wheels-for-linux).
|
||||
|
||||
And install [PyTorch Scatter](https://github.com/rusty1s/pytorch_scatter):
|
||||
```sh
|
||||
conda install pytorch-scatter -c pyg
|
||||
```bash
|
||||
git clone https://github.com/facebookresearch/pytorch3d.git
|
||||
cd pytorch3d
|
||||
module load compilers
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
|
||||
## Demo - Quick Start
|
||||
|
||||
First, run the script to get the demo data:
|
||||
|
@ -82,7 +83,7 @@ You can find the reconstrution on `out/demo_shapenet_outlier/generation/vis`.
|
|||
We have different dataset for our optimization-based and learning-based settings.
|
||||
|
||||
### Dataset for Optimization-based Reconstruction
|
||||
Here we consider the following dataset:
|
||||
Here we consider the following dataset:
|
||||
- [Thingi10K](https://arxiv.org/abs/1605.04797) (synthetic)
|
||||
- [Surface Reconstruction Benchmark (SRB)](https://github.com/fwilliams/deep-geometric-prior) (real scans)
|
||||
- [MPI Dynamic FAUST](https://dfaust.is.tue.mpg.de/) (real scans)
|
||||
|
@ -94,20 +95,20 @@ You can download the processed dataset (~200 MB) by running:
|
|||
bash scripts/download_optim_data.sh
|
||||
```
|
||||
|
||||
### Dataset for Learning-based Reconstruction
|
||||
### Dataset for Learning-based Reconstruction
|
||||
We train and evaluate on [ShapeNet](https://shapenet.org/).
|
||||
You can download the processed dataset (~220 GB) by running:
|
||||
```bash
|
||||
bash scripts/download_shapenet.sh
|
||||
```
|
||||
```
|
||||
After, you should have the dataset in `data/shapenet_psr` folder.
|
||||
|
||||
Alternatively, you can also preprocess the dataset yourself. To this end, you can:
|
||||
Alternatively, you can also preprocess the dataset yourself. To this end, you can:
|
||||
* first download the preprocessed dataset (73.4 GB) by running [the script](https://github.com/autonomousvision/occupancy_networks#preprocessed-data) from Occupancy Networks.
|
||||
* check [`scripts/process_shapenet.py`](https://github.com/autonomousvision/shape_as_points/tree/main/scripts/process_shapenet.py), modify the base path and run the code
|
||||
|
||||
|
||||
## Usage for Optimization-based 3D Reconstruction
|
||||
## Usage for Optimization-based 3D Reconstruction
|
||||
|
||||
For our optimization-based setting, you can consider running with a coarse-to-fine strategy:
|
||||
```python
|
||||
|
@ -124,7 +125,7 @@ You might need to modify the `CONFIG.yaml` accordingly.
|
|||
|
||||
## Usage for Learning-based 3D Reconstruction
|
||||
|
||||
### Mesh Generation
|
||||
### Mesh Generation
|
||||
To generate meshes using a trained model, use
|
||||
```python
|
||||
python generate.py configs/learning_based/CONFIG.yaml
|
||||
|
|
Loading…
Reference in a new issue