diff --git a/configs/local/inference_config.yaml b/configs/local/inference_config.yaml
index 8d78c32..13a7acc 100644
--- a/configs/local/inference_config.yaml
+++ b/configs/local/inference_config.yaml
@@ -20,18 +20,18 @@ runner:
   voxel_size: 0.003
   
 dataset:
-  OmniObject3d_train:
-    root_dir: "C:\\Document\\Datasets\\inference_test"
-    model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
-    source: seq_reconstruction_dataset_preprocessed
-    split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
-    type: test
-    filter_degree: 75
-    ratio: 1
-    batch_size: 1
-    num_workers: 12
-    pts_num: 8192
-    load_from_preprocess: True
+  # OmniObject3d_train:
+  #   root_dir: "C:\\Document\\Datasets\\inference_test1"
+  #   model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
+  #   source: seq_reconstruction_dataset_preprocessed
+  #   split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
+  #   type: test
+  #   filter_degree: 75
+  #   ratio: 1
+  #   batch_size: 1
+  #   num_workers: 12
+  #   pts_num: 8192
+  #   load_from_preprocess: True
 
   OmniObject3d_test:
     root_dir: "C:\\Document\\Datasets\\inference_test"
diff --git a/runners/inferencer.py b/runners/inferencer.py
index 0b990b5..a38b9e8 100644
--- a/runners/inferencer.py
+++ b/runners/inferencer.py
@@ -87,7 +87,7 @@ class Inferencer(Runner):
                     
             status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
         
-    def predict_sequence(self, data, cr_increase_threshold=0.001, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 7):
+    def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 7):
         scene_name = data["scene_name"]
         Log.info(f"Processing scene: {scene_name}")
         status_manager.set_status("inference", "inferencer", "scene", scene_name)
@@ -106,7 +106,6 @@ class Inferencer(Runner):
         
         ''' data for inference '''
         input_data = {}
-        scanned_pts = []
         input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)
         input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
         input_data["mode"] = namespace.Mode.TEST
@@ -119,7 +118,6 @@ class Inferencer(Runner):
         scanned_view_pts = [first_frame_target_pts]
         history_indices = [first_frame_scan_points_indices]
         last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
-        scanned_pts.append(first_frame_target_pts)
         retry_duplication_pose = []
         retry_no_pts_pose = []
         retry_overlap_pose = []
@@ -154,7 +152,6 @@ class Inferencer(Runner):
                     retry_overlap_pose.append(pred_pose.cpu().numpy().tolist())
                     continue
                 
-                scanned_pts.append(new_target_pts)
                 history_indices.append(new_scan_points_indices)
                 end_time = time.time()
                 print(f"Time taken for rendering: {end_time - start_time} seconds")
@@ -182,7 +179,7 @@ class Inferencer(Runner):
                 print("max coverage rate reached!: ", pred_cr)
                 success += 1
             elif new_added_pts_num < 5:
-                success += 1
+                #success += 1
                 print("min added pts num reached!: ", new_added_pts_num)
             if pred_cr <= last_pred_cr + cr_increase_threshold:
                 retry += 1
@@ -208,7 +205,6 @@ class Inferencer(Runner):
 
         input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
         result = {
-            "scanned_pts": scanned_pts,
             "pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
             "combined_scanned_pts": input_data["combined_scanned_pts"],
             "target_pts_seq": scanned_view_pts,