diff --git a/src/run_nerf.py b/src/run_nerf.py
index 0019d23..38bd35b 100644
--- a/src/run_nerf.py
+++ b/src/run_nerf.py
@@ -460,7 +460,8 @@ def train(args):
     else:
         T = poses_tensor.clone()
 
-    depth_confidences = cal_depth_confidences(torch.from_numpy(depth_priors).to(device), T, K, args.topk)
+    depth_confidences = cal_depth_confidences(torch.from_numpy(depth_priors).to(device), 
+                                              T, K, i_train, args.topk)
 
     print('DEFINING BOUNDS')
     if args.no_ndc:
diff --git a/utils/nerf_utils.py b/utils/nerf_utils.py
index 095482f..929a73b 100644
--- a/utils/nerf_utils.py
+++ b/utils/nerf_utils.py
@@ -20,28 +20,31 @@ def align_scales(depth_priors, colmap_depths, colmap_masks, poses, sc, i_train,
     depth_priors = depth_priors * sc * ratio_priors #align scales
     return depth_priors
 
-def cal_depth_confidences(depths, T, K, topk=4):
-    view_num, H, W = depths.shape
+def cal_depth_confidences(depths, T, K, i_train, topk=4):
+    _, H, W = depths.shape
+    view_num = len(i_train)
     invK = torch.inverse(K)
     batch_K = torch.unsqueeze(K, 0).repeat(view_num, 1, 1)
-    batch_invK = torch.unsqueeze(invK, 0).repeat(view_num, 1, 1)
-    invT = torch.inverse(T)
+    batch_invK = torch.unsqueeze(invK, 0).repeat(depths.shape[0], 1, 1)
+    T_train = T[i_train]
+    invT = torch.inverse(T_train)
     pix_coords = calculate_coords(W, H)
     cam_points = BackprojectDepth(depths, batch_invK, pix_coords)
     depth_confidences = []
 
-    for i in range(view_num):
+    for i in range(depths.shape[0]):
         cam_points_i = cam_points[i:i+1].repeat(view_num, 1, 1)
         T_i = torch.matmul(invT, T[i:i+1].repeat(view_num, 1, 1))
         pix_coords_ref = Project3D(cam_points_i, batch_K, T_i, H, W)
         depths_ = Project3D_depth(cam_points_i, batch_K, T_i, H, W)
-        depths_proj = F.grid_sample(depths.unsqueeze(1), pix_coords_ref,
+        depths_proj = F.grid_sample(depths[i_train].unsqueeze(1), pix_coords_ref,
                         padding_mode="zeros").squeeze()
         error = torch.abs(depths_proj - depths_) / (depths_ + 1e-7)
         depth_confidence, _ = error.topk(k=topk, dim=0, largest=False)
         depth_confidence =  depth_confidence.mean(0).cpu().numpy()
         depth_confidences.append(depth_confidence)
     return np.stack(depth_confidences, 0)
+    return np.stack(depth_confidences, 0)
 
 def calculate_coords(W, H):
     meshgrid = np.meshgrid(range(W), range(H), indexing='xy')