diff --git a/configs/server/server_train_config.yaml b/configs/server/server_train_config.yaml
index 2bf558d..190e6e3 100644
--- a/configs/server/server_train_config.yaml
+++ b/configs/server/server_train_config.yaml
@@ -116,16 +116,16 @@ module:
     feature_transform: False
 
   transformer_seq_encoder:
-    embed_dim: 384
+    embed_dim: 256
     num_heads: 4
     ffn_dim: 256
     num_layers: 3
-    output_dim: 2048
+    output_dim: 1024
 
   gf_view_finder:
     t_feat_dim: 128
     pose_feat_dim: 256
-    main_feat_dim: 3072
+    main_feat_dim: 2048
     regression_head: Rx_Ry_and_T
     pose_mode: rot_matrix
     per_point_feature: False
diff --git a/core/pipeline.py b/core/pipeline.py
index 164253c..797fd87 100644
--- a/core/pipeline.py
+++ b/core/pipeline.py
@@ -7,10 +7,10 @@ from PytorchBoot.factory.component_factory import ComponentFactory
 from PytorchBoot.utils import Log
 
 
-@stereotype.pipeline("nbv_reconstruction_global_pts_n_num_pipeline")
-class NBVReconstructionGlobalPointsPipeline(nn.Module):
+@stereotype.pipeline("nbv_reconstruction_pipeline")
+class NBVReconstructionPipeline(nn.Module):
     def __init__(self, config):
-        super(NBVReconstructionGlobalPointsPipeline, self).__init__()
+        super(NBVReconstructionPipeline, self).__init__()
         self.config = config
         self.module_config = config["modules"]
         
@@ -20,10 +20,6 @@ class NBVReconstructionGlobalPointsPipeline(nn.Module):
         self.pose_encoder = ComponentFactory.create(
             namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
         )
-        self.pts_num_encoder = ComponentFactory.create(
-            namespace.Stereotype.MODULE, self.module_config["pts_num_encoder"]
-        )
-        
         self.transformer_seq_encoder = ComponentFactory.create(
             namespace.Stereotype.MODULE, self.module_config["transformer_seq_encoder"]
         )
@@ -96,44 +92,21 @@ class NBVReconstructionGlobalPointsPipeline(nn.Module):
         scanned_n_to_world_pose_9d_batch = data[
             "scanned_n_to_world_pose_9d"
         ]  # List(B): Tensor(S x 9)
-        scanned_pts_mask_batch = data[
-            "scanned_pts_mask"
-        ]  # Tensor(B x N)
 
         device = next(self.parameters()).device
 
         embedding_list_batch = []
 
         combined_scanned_pts_batch = data["combined_scanned_pts"]  # Tensor(B x N x 3)
-        global_scanned_feat, perpoint_scanned_feat_batch = self.pts_encoder.encode_points(
-            combined_scanned_pts_batch, require_per_point_feat=True
-        )  # global_scanned_feat: Tensor(B x Dg), perpoint_scanned_feat: Tensor(B x N x Dl)
+        global_scanned_feat = self.pts_encoder.encode_points(
+            combined_scanned_pts_batch, require_per_point_feat=False
+        )  # global_scanned_feat: Tensor(B x Dg)
 
-        for scanned_n_to_world_pose_9d, scanned_mask, perpoint_scanned_feat in zip(
-            scanned_n_to_world_pose_9d_batch,
-            scanned_pts_mask_batch,
-            perpoint_scanned_feat_batch,
-        ):
-            scanned_target_pts_num = [] # List(S): Int
-            partial_feat_seq = []
-            
-            seq_len = len(scanned_n_to_world_pose_9d)
-            for seq_idx in range(seq_len): 
-                partial_idx_in_combined_pts = scanned_mask == seq_idx # Ndarray(V), N->V idx mask
-                partial_perpoint_feat = perpoint_scanned_feat[partial_idx_in_combined_pts] # Ndarray(V x Dl)
-                partial_feat = torch.mean(partial_perpoint_feat, dim=0) # Tensor(Dl)
-                partial_feat_seq.append(partial_feat)
-                scanned_target_pts_num.append(partial_perpoint_feat.shape[0])
-            
-                
-            scanned_target_pts_num = torch.tensor(scanned_target_pts_num, dtype=torch.float32).unsqueeze(-1).to(device) # Tensor(S x 1)
+        for scanned_n_to_world_pose_9d in scanned_n_to_world_pose_9d_batch:
             scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)  # Tensor(S x 9)
-            pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)  # Tensor(S x Dp)
-            pts_num_feat_seq = self.pts_num_encoder.encode_pts_num(scanned_target_pts_num)  # Tensor(S x Dn)
-            partial_feat_seq = torch.stack(partial_feat_seq) # Tensor(S x Dl)
-            seq_embedding = torch.cat([pose_feat_seq, pts_num_feat_seq, partial_feat_seq], dim=-1) # Tensor(S x (Dp+Dn+Dl))
-            embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp+Dn+Dl))
-
+            pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)  # Tensor(S x Dp) 
+            seq_embedding = pose_feat_seq
+            embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp))
         
         seq_feat = self.transformer_seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
         main_feat = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))