Merge branch 'master' into master

run_nerf.py

@@ -44,11 +44,12 @@ def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn, netchunk=1024*64):
     return outputs
 
 
-def render_rays(ray_batch,
-                network_fn,
+
+def render_rays(ray_batch, 
+                network_fn, 
                 network_query_fn,
-                N_samples,
-                retraw=False,
+                N_samples, 
+                retraw=False, 
                 lindisp=False,
                 perturb=0.,
                 N_importance=0,

run_nerf_helpers.py

@@ -104,8 +104,10 @@ def dense(W, act=relu): return tf.keras.layers.Dense(W, activation=act)
         bottleneck = dense(256, act=None)(outputs)
         inputs_viewdirs = tf.concat(
             [bottleneck, inputs_views], -1)  # concat viewdirs
-        for i in range(D//2):
-            outputs = dense(W//2)(inputs_viewdirs)
+        # The supplement to the paper states there are 4 hidden layers here, but this is an error since
+        # the experiments were actually run with 1 hidden layer, so we will leave it as 1.
+        for i in range(1):
+            outputs = dense(W//2)(outputs)
         outputs = dense(3, act=None)(outputs)
         outputs = tf.concat([outputs, alpha_out], -1)
     else: