diff --git a/.gitignore b/.gitignore
index 6232d11..08ebda8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@
 *.pyc
 data/
 test.dae
-mesh_objects.py
\ No newline at end of file
+mesh_objects.py
+out/
\ No newline at end of file
diff --git a/mise.py b/mise.py
index c19b961..94ea336 100644
--- a/mise.py
+++ b/mise.py
@@ -17,14 +17,10 @@
 from visualization import plot_3d_points, plot_voxel, convert_to_sparse_voxel_grid, visualize_points_overlay
 from sdf_dataset import get_sdf_dataset, get_pcd
 
-from embedding import cloud_embedding
-
-sys.path.append('/home/markvandermerwe/catkin_ws/src/ll4ma_3d_reconstruction/src/data_generation/')
-from generate_view_splits import get_view_splits
-
 _MODEL_FUNC = get_pointconv_model
 _MODEL_PATH = '/home/markvandermerwe/models/ICRA_Models/reconstruction/pointconv_mse_cf'
-_SAVE_PATH = '/home/markvandermerwe/data/ReconstructedMeshesTest/Real'
+_SPLITS_PATH = '/home/markvandermerwe/catkin_ws/src/ll4ma_3d_reconstruction/src/data_generation/data_split/test_fold.txt'
+_SAVE_PATH = 'out/test_dir/'
 _PCD_DATABASE = '/dataspace/ICRA_Data/PyrenderData/Depth/'
 _GRASP_DATABASE = False
 _OBJECT_FRAME = False
@@ -198,7 +194,7 @@ def mise_voxel(get_sdf, bound, initial_voxel_resolution, final_voxel_resolution,
 
 def get_test_meshes(grasp_database=True, ycb_database=False):
     meshes = set()
-    with open('/home/markvandermerwe/catkin_ws/src/ll4ma_3d_reconstruction/src/data_generation/data_split/test_fold.txt') as f:
+    with open(_SPLITS_PATH) as f:
         for view in f:
             if grasp_database and 'poisson' in view:
                 meshes.add('_'.join(view.split('_')[:-1]))
@@ -207,22 +203,16 @@ def get_test_meshes(grasp_database=True, ycb_database=False):
     
     fin_meshes = []
     for mesh in meshes:
-        fin_meshes.append(mesh + '_10')
+        fin_meshes.append(mesh + '_10') # Only use the 10th rendered view.
         
     return fin_meshes
-
-def get_real_pt_cld():
-    real_info = "/home/markvandermerwe/data/GraspTestData/recon_high/mustard_p1_a1/grasp_plan_info.pickle"
-    obj_dict = pickle.load(open(real_info))
-    return obj_dict['scaled_object_cloud'], (obj_dict['max_dim'] * (1.03/1.0)), obj_dict['scale'], [0,0,0]
         
 def mesh_objects(model_func, model_path, save_path, pcd_folder, grasp_database=True):
     # Setup model.
     get_sdf, get_embedding, _ = get_sdf_prediction(model_func, model_path)
 
     # Get names of partial views.
-    # meshes = get_test_meshes(grasp_database=grasp_database, ycb_database=(not grasp_database))
-    meshes = ["mustard_real"]
+    meshes = get_test_meshes(grasp_database=grasp_database, ycb_database=(not grasp_database))
     
     # Bounds of 3D space to evaluate in: [-bound, bound] in each dim.
     bound = 0.8
@@ -234,8 +224,7 @@ def mesh_objects(model_func, model_path, save_path, pcd_folder, grasp_database=T
     # Mesh the views.
     for mesh in tqdm(meshes):
         # Point cloud for this view.
-        # pc_, length, scale, centroid_diff = get_pcd(mesh, pcd_folder, object_frame=_OBJECT_FRAME, verbose=False);
-        pc_, length, scale, centroid_diff = get_real_pt_cld()
+        pc_, length, scale, centroid_diff = get_pcd(mesh, pcd_folder, object_frame=_OBJECT_FRAME, verbose=False);
         
         voxel_size = (2.*bound * length) / float(final_voxel_resolution)    
         if pc_ is None:
diff --git a/run_sdf_model.py b/run_sdf_model.py
index 49797a0..19d3d7a 100644
--- a/run_sdf_model.py
+++ b/run_sdf_model.py
@@ -13,7 +13,7 @@
 from helper import get_num_trainable_variables, shuffle_in_unison, get_bn_decay, get_learning_rate
 from visualization import plot_3d_points, plot_voxel, convert_to_sparse_voxel_grid
 
-def run(get_model, train_path, validation_path, model_path, logs_path, batch_size=32, epoch_start=0, epochs=100, learning_rate=1e-4, optimizer='adam', train=True, warm_start=False, alpha=0.5, loss_function='mse', sdf_count=64):
+def run_sdf(get_model, train_path, validation_path, model_path, logs_path, batch_size=32, epoch_start=0, epochs=100, learning_rate=1e-4, optimizer='adam', train=True, warm_start=False, sdf_count=64, voxel=False):
 
     # Read in training and validation files.
     train_files = [os.path.join(train_path, filename) for filename in os.listdir(train_path) if ".tfrecord" in filename]
@@ -48,7 +48,7 @@ def run(get_model, train_path, validation_path, model_path, logs_path, batch_siz
     xyz_in = tf.placeholder(tf.float32, name="query_points")
     sdf_labels = tf.placeholder(tf.float32, name="query_labels")
     is_training = tf.placeholder(tf.bool, name="is_training")
-    sdf_prediction, loss, debug = get_model(points, xyz_in, sdf_labels, is_training, bn_decay, batch_size=batch_size, alpha=alpha, loss_function=loss_function)
+    sdf_prediction, loss, debug = get_model(points, xyz_in, sdf_labels, is_training, bn_decay, batch_size=batch_size)
 
     # Get update ops for the BN.
     update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
@@ -92,7 +92,6 @@ def run(get_model, train_path, validation_path, model_path, logs_path, batch_siz
             # Track loss throughout updates.
             total_loss = 0.0
             examples = 0
-            
             while True:
                 try:
                     # Split the given features into batches.
@@ -115,8 +114,8 @@ def run(get_model, train_path, validation_path, model_path, logs_path, batch_siz
 
                         plot_3d_points(point_clouds_[0])
                         plot_3d_points(pts)
-                        plot_3d_points(pts, truth)
-                        plot_3d_points(pts, pred)
+                        plot_3d_points(pts, signed_distances=truth)
+                        plot_3d_points(pts, signed_distances=pred)
 
                     total_loss += loss_