Datasets:

tfnn
/

HeadsNet

@@ -102,4 +102,4 @@ More about this type of network topology can be read here: https://gist.github.c
 * Future networks will have 3 additional input parameters one for each x,y,z of a unit vector for the ray direction from the icosphere index.
 * The unit vector used to train the network will just be the vertex normal from the 3D model but inverted.
 * When performing inference more forward-passes would need to be performed as some density of rays in a 30° or similar cone angle pointing to 0,0,0 would need to be performed per icosphere index position.
-* This could lead to higher quality outputs.

 * Future networks will have 3 additional input parameters one for each x,y,z of a unit vector for the ray direction from the icosphere index.
 * The unit vector used to train the network will just be the vertex normal from the 3D model but inverted.
 * When performing inference more forward-passes would need to be performed as some density of rays in a 30° or similar cone angle pointing to 0,0,0 would need to be performed per icosphere index position.
+* This could result in higher quality outputs.