diff --git a/nerfactory/configs/base.py b/nerfactory/configs/base.py
index 39ab0c2ac..034942361 100644
--- a/nerfactory/configs/base.py
+++ b/nerfactory/configs/base.py
@@ -293,7 +293,7 @@ class InstantNGPDataParserConfig(DataParserConfig):
 
 @dataclass
 class Record3DDataParserConfig(DataParserConfig):
-    """Mipnerf 360 dataset config"""
+    """Record3D dataset config"""
 
     _target: Type = Record3D
     """target class to instantiate"""
diff --git a/nerfactory/datamanagers/dataparsers/record3d_parser.py b/nerfactory/datamanagers/dataparsers/record3d_parser.py
index e5ae26410..55598fa98 100644
--- a/nerfactory/datamanagers/dataparsers/record3d_parser.py
+++ b/nerfactory/datamanagers/dataparsers/record3d_parser.py
@@ -21,11 +21,12 @@ import numpy as np
 import torch
 from scipy.spatial.transform import Rotation
 
+from nerfactory.cameras import utils as camera_utils
 from nerfactory.cameras.cameras import Cameras, CameraType
 from nerfactory.configs import base as cfg
 from nerfactory.datamanagers.dataparsers.base import DataParser
-from nerfactory.datamanagers.dataparsers.mipnerf_parser import Mipnerf360
 from nerfactory.datamanagers.structs import DatasetInputs, SceneBounds
+from nerfactory.utils import poses as pose_utils
 from nerfactory.utils.io import get_absolute_path, load_from_json
 
 
@@ -70,24 +71,6 @@ class Record3D(DataParser):
             image_filenames = image_filenames[idx]
             num_images = len(image_filenames)
 
-        # Normalization similar to Mipnerf360
-        poses = Mipnerf360.normalize_orientation(poses)
-
-        bottom = np.reshape([0, 0, 0, 1.0], [1, 4])
-        bottom = np.tile(np.reshape(bottom, [1, 1, 4]), [poses.shape[0], 1, 1])
-        poses = np.concatenate([poses[:, :3, :4], bottom], -2).astype(np.float32)
-
-        rotation_matrix = np.array(
-            [
-                [1.0, 0.0, 0.0, 0.0],
-                [0.0, 0.0, -1.0, 0.0],
-                [0.0, 1.0, 0.0, 0.0],
-                [0.0, 0.0, 0.0, 1.0],
-            ],
-            dtype=np.float32,
-        )
-        poses = rotation_matrix @ poses
-
         idx_test = np.arange(num_images)[:: self.config.val_skip]
         idx_train = np.array([i for i in np.arange(num_images) if i not in idx_test])
         idx = idx_train if split == "train" else idx_test
@@ -97,10 +80,19 @@ class Record3D(DataParser):
         image_filenames = image_filenames[idx]
         poses = poses[idx]
 
-        # Centering poses
-        poses[:, :3, 3] = poses[:, :3, 3] - np.mean(poses[:, :3, 3], axis=0)
+        # convert to Tensors
+        poses = torch.from_numpy(poses[:, :3, :4])
 
-        camera_to_world = torch.from_numpy(poses[:, :3, :4])  # camera to world transform
+        # convert OpenGL to OpenCV coordinate system
+        poses = poses @ torch.diag(torch.tensor([1.0, -1.0, -1.0, 1.0]))
+
+        poses = camera_utils.auto_orient_poses(pose_utils.to4x4(poses), method=self.config.orientation_method)[
+            :, :3, :4
+        ]
+
+        # Centering poses
+        poses[:, :3, 3] = poses[:, :3, 3] - torch.mean(poses[:, :3, 3], dim=0)
+        poses = pose_utils.normalize(poses)
 
         # Camera intrinsics
         K = np.array(metadata_dict["K"]).reshape((3, 3)).T
@@ -127,7 +119,7 @@ class Record3D(DataParser):
             fy=focal_length,
             cx=cx,
             cy=cy,
-            camera_to_worlds=camera_to_world,
+            camera_to_worlds=poses,
             camera_type=CameraType.PERSPECTIVE,
         )