diff --git a/Create_DSM.py b/Create_DSM.py
new file mode 100644
index 0000000..1a36ae2
--- /dev/null
+++ b/Create_DSM.py
@@ -0,0 +1,69 @@
+import numpy as np
+from PIL import Image
+import open3d as o3d
+from tqdm import tqdm
+
+# Output paths: Set the paths for saving the results
+output_ply_path = 'your_path/output.ply'  # Path for the point cloud PLY file
+output_mesh_ply_path = 'your_path/mesh_output.ply'  # Path for the high-quality mesh PLY file
+output_mesh_obj_path = 'your_path/mesh_output.obj'  # Path for the high-quality mesh OBJ file
+
+# Display progress bar for DSM Image Load
+print("Loading DSM Image...")
+with tqdm(total=100, desc="Progress", ncols=100) as pbar:
+    dsm_image = Image.open('your_tif_path/.tif')
+    dsm_array = np.array(dsm_image)
+    pbar.update(20)  # 20% completed
+
+    
+
+# Check if the DSM array is empty or not
+if dsm_array.size == 0:
+    raise ValueError("DSM image data is empty. Please check the input file.")
+
+# Create Ply using vectorized numpy operations: Generate point cloud using vectorized operations
+print("Creating Point Cloud...")
+h, w = dsm_array.shape  # Get the height and width of the DSM image
+x, y = np.meshgrid(np.arange(w), np.arange(h))  # Create a grid of x and y coordinates
+z = dsm_array.flatten()  # Flatten the elevation data into a 1D array
+points = np.vstack((x.flatten(), y.flatten(), z)).T  # Combine x, y, z coordinates into a single array to form points
+pbar.update(20)  # 40% completed
+
+# PointCloud Object Creation: Create a point cloud object using Open3D
+print("Creating PointCloud Object...")
+point_cloud = o3d.geometry.PointCloud()
+point_cloud.points = o3d.utility.Vector3dVector(points)  # Set the coordinates in the point cloud
+pbar.update(20)  # 60% completed
+
+# **Normal Estimation**: Estimate normals for the point cloud
+print("Estimating Normals...")
+point_cloud.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=1.0, max_nn=30))
+# The radius and max_nn parameters define the search radius and the number of nearest neighbors to consider; adjust if needed
+pbar.update(10)  # 70% completed
+
+# Poisson Surface Reconstruction with GPU (if available): Generate a mesh using GPU
+print("Performing Poisson Surface Reconstruction...")
+mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(point_cloud, depth=12, scale=1.1)
+# The depth and scale parameters control the level of detail and scaling of the mesh; adjust if needed
+pbar.update(10)  # 80% completed
+
+# Check if densities array is empty to avoid errors
+densities_np = np.asarray(densities)
+if densities_np.size == 0:
+    raise ValueError("Densities array is empty. Poisson reconstruction might have failed. Check the input point cloud.")
+
+# Remove low-density vertices to refine the mesh
+print("Refining Mesh...")
+vertices_to_remove = densities_np < np.quantile(densities_np, 0.01)  # Identify vertices to remove based on density threshold
+if np.any(vertices_to_remove):
+    mesh.remove_vertices_by_mask(vertices_to_remove)  # Remove the identified vertices from the mesh
+pbar.update(10)  # 90% completed
+
+# Save high-quality mesh to PLY and OBJ files
+print("Saving Mesh and Point Cloud...")
+o3d.io.write_triangle_mesh(output_mesh_ply_path, mesh)  # Save as PLY
+o3d.io.write_triangle_mesh(output_mesh_obj_path, mesh)  # Save as OBJ
+o3d.io.write_point_cloud(output_ply_path, point_cloud)  # Save the original point cloud to a PLY file
+pbar.update(10)  # 100% completed
+
+print("Process Completed.")
diff --git a/README.md b/README.md
index 10f424e..c5ba508 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,18 @@
 &#128293; &#128293; **UPDATE JUNE 2023 !!!** Have a look at [EO-NeRF](https://rogermm14.github.io/eonerf/), our latest method for multi-view satellite photogrammetry using neural radiance fields. &#128293; &#128293;
 
 ---
+### Updates and Fixes in the Project
+Issue: TypeError with SSIM Calculation
+During the execution of the training script, a TypeError: 'module' object is not callable was encountered. This issue was identified within the metrics.py file, specifically in the ssim function. The error occurred because the alias ssim_ for the kornia.losses.ssim function was mistakenly treated as a callable function.
+
+Additional Adjustments in main.py
+In addition to fixing the TypeError, other enhancements and clarifications were made in the main.py file to improve the overall functionality and readability of the script:
+
+Ensured proper initialization and configuration of the training setup.
+Enhanced logging for better traceability of the training process.
+Improved handling of training and validation steps to ensure smooth execution.
+Adjusted the code to accommodate various configurations and parameters as required by the project.
+
 
 ### [[Project page]](https://centreborelli.github.io/satnerf)
 
diff --git a/metrics.py b/metrics.py
index 0863d79..de6dff4 100644
--- a/metrics.py
+++ b/metrics.py
@@ -3,7 +3,7 @@
 """
 
 import torch
-from kornia.losses import ssim as ssim_
+from kornia.losses import ssim_loss
 
 class NerfLoss(torch.nn.Module):
     def __init__(self):
@@ -118,4 +118,4 @@ def ssim(image_pred, image_gt):
     image_pred and image_gt: (1, 3, H, W)
     important: kornia==0.5.3
     """
-    return torch.mean(ssim_(image_pred, image_gt, 3))
+    return torch.mean(ssim_loss(image_pred, image_gt, 3))