diff --git a/packnet_sfm/losses/supervised_loss.py b/packnet_sfm/losses/supervised_loss.py
index 4ff67918..11cca802 100644
--- a/packnet_sfm/losses/supervised_loss.py
+++ b/packnet_sfm/losses/supervised_loss.py
@@ -6,8 +6,102 @@
 from packnet_sfm.utils.image import match_scales
 from packnet_sfm.losses.loss_base import LossBase, ProgressiveScaling
 
+from packnet_sfm.utils.depth import inv2depth
+from packnet_sfm.utils.image import image_grid 
 ########################################################################################################################
 
+class ReprojectedDistanceLoss(nn.Module):
+  def __init__(self):
+    super().__init__()
+
+  def warp(self, depth, K, Kinv, T):
+    """
+    Warps pixels from one image plane to another one.
+    
+    Parameters
+    ----------
+    depth : torch.Tensor [B,1,H,W]
+        Depth map for the camera
+    
+    T : torch.Tensor 
+      Relative pose
+
+    K : torch.Tensor
+      Intrinsics matrix
+
+    Kinv : torch.Tensor
+      Inverse of the intrinsics matrix
+
+    Returns
+    -------
+    points : torch.Tensor [B,H,W,2]
+            2D projected points that are within the image boundaries
+    """
+
+    B, _, H, W = depth.shape
+
+    # Create flat index grid
+    grid = image_grid(B, H, W, depth.dtype, depth.device, normalized=False)  # [B,3,H,W]
+    flat_grid = grid.view(B, 3, -1)  # [B,3,HW]
+
+    # Estimate the outward rays in the camera frame
+    xnorm = (self.Kinv.bmm(flat_grid)).view(B, 3, H, W)
+    # Scale rays to metric depth
+    Xc = xnorm * depth
+
+    # Project the 3D point
+    Xs = T @ Xc
+    Xs = self.K.bmm(Xs.view(B, 3, -1))
+    # Normalize points
+    X = Xs[:, 0]
+    Y = Xs[:, 1]
+    Z = Xs[:, 2].clamp(min=1e-5)
+    Xnorm = 2 * (X / Z) / (W - 1) - 1.
+    Ynorm = 2 * (Y / Z) / (H - 1) - 1.
+
+    return torch.stack([Xnorm, Ynorm], dim=-1).view(B, H, W, 2)
+
+  def forward(self, inv_depth_gt, inv_depth_est, K, Kinv, T_ts):
+    """
+    Calculates the reprojected distance loss.
+
+    Parameters
+    ----------
+    inv_depth_est : torch.Tensor [B,1,H,W]
+        Predicted inverse depth map
+    inv_depth_gt : torch.Tensor [B,1,H,W]
+        Ground-truth inverse depth map
+    K : torch.Tensor 
+      Intrinsic parameter
+
+    Kinv : torch.Tensor 
+      Inverse of the intrinsics matrix
+
+    T_ts : torch.Tensor
+      Relative pose transformation from the source image camera frame to the target
+      image camera frame
+      
+    
+    Returns
+    -------
+    loss : torch.Tensor [1]
+        reprojected distance loss
+    """
+
+    depth_gt = inv2depth(inv_depth_gt)
+    depth_est = inv2depth(inv_depth_est)
+
+    # Warp pixels from target frame to source frame
+    # Convert pose from source-to-target into target-to-source
+    T_st = torch.linalg.inv(T_ts)
+    p_gt = self.warp(depth_gt, K, Kinv, T_st)
+    p_est = self.warp(depth_est, K, Kinv, T_st)
+
+    loss_tmp = (p_est - p_gt).abs()
+    return loss_tmp.mean()
+
+
+
 class BerHuLoss(nn.Module):
     """Class implementing the BerHu loss."""
     def __init__(self, threshold=0.2):
@@ -179,4 +273,4 @@ def forward(self, inv_depths, gt_inv_depth,
         return {
             'loss': loss.unsqueeze(0),
             'metrics': self.metrics,
-        }
\ No newline at end of file
+        }