Fix GEOmetrics Image Recon example (#68)

* Fix GeoMetrics example

Signed-off-by: Jean-Francois Lafleche <jlafleche@nvidia.com>

* Code style fixes

Signed-off-by: Jean-Francois Lafleche <jlafleche@nvidia.com>

* Improve readability of realin func

Signed-off-by: Jean-Francois Lafleche <jlafleche@nvidia.com>

* Style cleanup

Signed-off-by: Jean-Francois Lafleche <jlafleche@nvidia.com>

.gitignore

@@ -193,4 +193,8 @@ public/
 **/.vscode/**
 
 # Results directory for examples
-examples/renderers/DIB-R/results/
+examples/renderers/DIB-R/results/
+
+# example outputs
+cache/
+log/

examples/ImageRecon/GEOmetrics/README.md

@@ -1,31 +1,54 @@
 # Example: GeoMetrics
-This example allows you to train and test the GEOMetrice algorithm for signle image 3D object reconstruction. For details on the algorithms see: "GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects" : https://arxiv.org/abs/1901.11461
+This example includes training and testing of the GEOMetrics algorithm for 
+single image 3D object reconstruction. For details on the algorithms see: 
+"GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects": 
+https://arxiv.org/abs/1901.11461
+
+Training on a single class takes several days using an NVIDIA RTX 2080Ti
+GPU. To obtain more visually pleasing results at the expense of F-Score,  
+increase the regularizer weights (laplace, edge).
+
+Note: `cache-dir` specifies the location to which intermediate asset
+representations will be stored.
 
 
 ### Training the network:
 
-To train the system call
-```
-python train.py
+#### Option 1 : No latent embedding loss (faster to train)
+```bash
+python train.py --shapenet-root <path/to/ShapeNet> \
+    --shapenet-images-root <path/to/ShapeNetImages> \
+    --categories <category list> \
+    --cache-dir cache/
 ```
 
 
-To train the system with the latent encoding loss call first: 
+#### Option 2 : With latent embedding loss (better results)
+```bash
+python train_auto_encoder.py --shapenet-root <path/to/ShapeNet> \
+    --cache-dir cache/
 ```
-python train_auto_encoder.py
-```
-This will train the mesh encoder. Then call the follwoing to train the image reconstruction algorithm: 
-```
-python train.py -latent_loss
+This will train the mesh encoder. Then call the following to train the image 
+reconstruction algorithm: 
+```bash
+python train.py --shapenet-root <path/to/ShapeNet> \
+    --shapenet-images-root <path/to/ShapeNetImages> \
+    --categories <category list> \
+    --cache-dir cache/ --latent_loss
 ```
 
 ### Evaluating the network: 
-
-To evaluate a trained model
+To evaluate the auto-encoder
+```bash
+python eval_auto_encoder.py --shapenet-root <path/to/ShapeNet> \
+    --categories <category list> \
+    --cache-dir cache/
 ```
-python eval.py
+To evaluate a trained model
+(Note, in the Trimesh window, tap `c` to disable backface culling)
+```bash
+python eval.py --shapenet-root <path/to/ShapeNet> \
+    --shapenet-images-root <path/to/ShapeNetImages> \
+    --categories <category list> \
+    --cache-dir cache/
 ```
-
-
-
-