Remove mesh-to-sdf from install_requires and install it manually for …

…preprocessing

README.md

@@ -86,6 +86,7 @@ To train a network for a specific category you need to first train a per-categor
 #### VAE
 First we need to convert the ShapeNet meshes to SDFs and optionally filter the dataset. To reproduce the preprocessing of the paper run
 ```bash
+pip install git+https://github.com/marian42/mesh_to_sdf.git
 source preprocess_shapenet.sh
 ```
 Then run
@@ -138,6 +139,7 @@ Architecture to learn a low-dimensional representation of signed-distance fields
 
 You need to preprocess the mesh data prior to running the training script, like this:
 ```bash
+pip install git+https://github.com/marian42/mesh_to_sdf.git
 python -m sdfest.vae.scripts.process_shapenet --inpath shapenet_subfolder --outpath output_path --resolution 64 --padding 2
 ```
 You can control the resolution and added padding so that there is some empty space left in the signed distance field on all borders. If you are running this script via ssh you need to run `export PYOPENGL_PLATFORM=egl` prior to running the script and use the `--all` option which will disable any filtering. Otherwise mesh selection will proceed in two steps: first you see one mesh after another and need to decide which to keep. Pressing left will remove a mesh, pressing right will keep it. After a decision has been made, the conversion will run. Finally another manual selection process is started, where you can remove SDFs in which the mesh to SDF conversion has failed. 

setup.py

@@ -12,7 +12,7 @@
 
 setuptools.setup(
     name="sdfest",
-    version="0.1.0",
+    version="0.1.1",
     author="Leonard Bruns",
     author_email="[email protected]",
     description="6-DoF pose, scale, and shape estimation architecture",
@@ -33,7 +33,7 @@
         "healpy",
         "joblib",
         "matplotlib",
-        "mesh-to-sdf",
+        # "mesh-to-sdf, removed due to https://github.com/marian42/mesh_to_sdf/issues/38
         "ninja",
         "numpy",
         "open3d",