diff --git a/utils/ply_utils.py b/utils/ply_utils.py
index c18f8f9..663bc4b 100644
--- a/utils/ply_utils.py
+++ b/utils/ply_utils.py
@@ -1,4 +1,6 @@
 from array import array
+import plyfile
+import numpy as np
 
 import torch
 
@@ -12,24 +14,14 @@ def __init__(self, height, width, min_d=3, max_d=400, batch_size=1, roi=None, dr
         self.max_d = max_d
         self.roi = roi
         self.dropout = dropout
-        self.data = array('f')
+        self.data = []
 
         self.projector = Backprojection(batch_size, height, width)
 
     def save(self, file):
-        length = len(self.data) // 6
-        header = "ply\n" \
-                 "format binary_little_endian 1.0\n" \
-                 f"element vertex {length}\n" \
-                 f"property float x\n" \
-                 f"property float y\n" \
-                 f"property float z\n" \
-                 f"property float red\n" \
-                 f"property float green\n" \
-                 f"property float blue\n" \
-                 f"end_header\n"
-        file.write(header.encode(encoding="ascii"))
-        self.data.tofile(file)
+        vertices = np.array(list(map(tuple, self.data)),  dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')])
+        vertex_el = plyfile.PlyElement.describe(vertices, 'vertex')
+        plyfile.PlyData([vertex_el]).write(file)
 
     def add_depthmap(self, depth: torch.Tensor, image: torch.Tensor, intrinsics: torch.Tensor,
                      extrinsics: torch.Tensor):
@@ -39,7 +31,7 @@ def add_depthmap(self, depth: torch.Tensor, image: torch.Tensor, intrinsics: tor
         if self.roi is not None:
             mask[:, :, :self.roi[0], :] = False
             mask[:, :, self.roi[1]:, :] = False
-            mask[:, :, :, :self.roi[2]] = False
+            mask[:, :, :, self.roi[2]] = False
             mask[:, :, :, self.roi[3]:] = False
         if self.dropout > 0:
             mask = mask & (torch.rand_like(depth) > self.dropout)
@@ -48,6 +40,6 @@ def add_depthmap(self, depth: torch.Tensor, image: torch.Tensor, intrinsics: tor
         coords = extrinsics @ coords
         coords = coords[:, :3, :]
         data_batch = torch.cat([coords, image.view_as(coords)], dim=1).permute(0, 2, 1)
-        data_batch = data_batch[mask.view(depth.shape[0], 1, -1).permute(0, 2, 1).expand(-1, -1, 6)]
+        data_batch = data_batch.view(-1, 6)[mask.view(-1), :]
 
         self.data.extend(data_batch.cpu().tolist())