From 43164f892f809526f4c512181697ea1b9ffb5669 Mon Sep 17 00:00:00 2001
From: Laurent FAINSIN <laurent.fainsin@safrangroup.com>
Date: Tue, 30 May 2023 11:46:06 +0200
Subject: [PATCH] =?UTF-8?q?=F0=9F=93=9D=20update=20README.md=20for=20bette?=
 =?UTF-8?q?r=20installation=20instructions?=
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---
 README.md | 31 ++++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index 1f3e822..e996999 100644
--- a/README.md
+++ b/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