diff --git a/compute_gt_poses.py b/compute_gt_poses.py
index 24e2faf..305746d 100644
--- a/compute_gt_poses.py
+++ b/compute_gt_poses.py
@@ -149,9 +149,9 @@ def full_registration(path,max_correspondence_distance_coarse,
                       max_correspondence_distance_fine):
 
      global N_Neighbours, LABEL_INTERVAL, n_pcds
-     pose_graph = registration.PoseGraph()
+     pose_graph = pipelines.registration.PoseGraph()
      odometry = np.identity(4)
-     pose_graph.nodes.append(registration.PoseGraphNode(odometry))
+     pose_graph.nodes.append(pipelines.registration.PoseGraphNode(odometry))
 
      pcds = [[] for i in range(n_pcds)]
      for source_id in trange(n_pcds):
@@ -183,19 +183,19 @@ def full_registration(path,max_correspondence_distance_coarse,
 
                else:
                     transformation_icp = res
-                    information_icp = registration.get_information_matrix_from_point_clouds(
+                    information_icp = pipelines.registration.get_information_matrix_from_point_clouds(
                          pcds[source_id], pcds[target_id], max_correspondence_distance_fine,
                          transformation_icp)
 
                if target_id == source_id + 1:
                     # odometry
                     odometry = np.dot(transformation_icp, odometry)
-                    pose_graph.nodes.append(registration.PoseGraphNode(np.linalg.inv(odometry)))
-                    pose_graph.edges.append(registration.PoseGraphEdge(source_id, target_id,
+                    pose_graph.nodes.append(pipelines.registration.PoseGraphNode(np.linalg.inv(odometry)))
+                    pose_graph.edges.append(pipelines.registration.PoseGraphEdge(source_id, target_id,
                                                           transformation_icp, information_icp, uncertain = False))
                else:
                     # loop closure
-                    pose_graph.edges.append(registration.PoseGraphEdge(source_id, target_id,
+                    pose_graph.edges.append(pipelines.registration.PoseGraphEdge(source_id, target_id,
                                                           transformation_icp, information_icp, uncertain = True))
 
      return pose_graph
@@ -233,7 +233,7 @@ def load_pcds(path, downsample = True, interval = 1):
     global voxel_size, camera_intrinsics 
     pcds= []
     
-    for Filename in xrange(len(glob.glob1(path+"JPEGImages","*.jpg"))/interval):
+    for Filename in range(len(glob.glob1(path+"JPEGImages","*.jpg"))/interval):
         img_file = path + 'JPEGImages/%s.jpg' % (Filename*interval)
         
         cad = cv2.imread(img_file)
@@ -352,13 +352,13 @@ def print_usage():
                                        max_correspondence_distance_fine)
 
         print("Optimizing PoseGraph ...")
-        option =registration.GlobalOptimizationOption(
+        option =pipelines.registration.GlobalOptimizationOption(
                 max_correspondence_distance = max_correspondence_distance_fine,
                 edge_prune_threshold = 0.25,
                 reference_node = 0)
-        registration.global_optimization(pose_graph,
-                                         registration.GlobalOptimizationLevenbergMarquardt(),
-                                         registration.GlobalOptimizationConvergenceCriteria(), option)
+        pipelines.registration.global_optimization(pose_graph,
+                                         pipelines.registration.GlobalOptimizationLevenbergMarquardt(),
+                                         pipelines.registration.GlobalOptimizationConvergenceCriteria(), option)
 
 
 
diff --git a/get_BBs.py b/get_BBs.py
index 4bf749e..85a9c1c 100644
--- a/get_BBs.py
+++ b/get_BBs.py
@@ -24,7 +24,7 @@
             mask = cv2.imread(mask_dir,0)
             thresh = cv2.threshold(mask.copy(), 30, 255, cv2.THRESH_BINARY)[1]
 
-            _, contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+            contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
             cnt = max(contours, key=cv2.contourArea)
             x,y,w,h = cv2.boundingRect(cnt)
             cv2.rectangle(img_original,(x,y),(x+w,y+h),(0,255,0),2)
diff --git a/registration.py b/registration.py
index 1ce2a84..6c1d86b 100644
--- a/registration.py
+++ b/registration.py
@@ -46,25 +46,25 @@ def icp(source,target,voxel_size,max_correspondence_distance_coarse,max_correspo
 
     assert method in ["point-to-plane","colored-icp"],"point-to-plane or colored-icp"
     if method == "point-to-plane":
-        icp_coarse = registration.registration_icp(source, target,
+        icp_coarse = pipelines.registration.registration_icp(source, target,
                                                    max_correspondence_distance_coarse, np.identity(4),
-                                                   registration.TransformationEstimationPointToPlane())
-        icp_fine = registration.registration_icp(source, target,
+                                                   pipelines.registration.TransformationEstimationPointToPlane())
+        icp_fine = pipelines.registration.registration_icp(source, target,
                 max_correspondence_distance_fine, icp_coarse.transformation,
-                registration.TransformationEstimationPointToPlane())
+                pipelines.registration.TransformationEstimationPointToPlane())
 
         transformation_icp = icp_fine.transformation
 
 
     if method == "colored-icp":
-        result_icp = registration.registration_colored_icp(source,target,voxel_size, np.identity(4),
-                                                           registration.ICPConvergenceCriteria(relative_fitness = 1e-8,
+        result_icp = pipelines.registration.registration_colored_icp(source,target,voxel_size, np.identity(4),
+                                                           pipelines.registration.ICPConvergenceCriteria(relative_fitness = 1e-8,
                                                                                                relative_rmse = 1e-8, max_iteration = 50))
 
         transformation_icp = result_icp.transformation
 
         
-    information_icp = registration.get_information_matrix_from_point_clouds(
+    information_icp = pipelines.registration.get_information_matrix_from_point_clouds(
         source, target, max_correspondence_distance_fine,
         transformation_icp)