diff --git a/nerfstudio/lantern/dataset.py b/nerfstudio/lantern/dataset.py
index f5f0b362d..f0e1fd49c 100644
--- a/nerfstudio/lantern/dataset.py
+++ b/nerfstudio/lantern/dataset.py
@@ -151,6 +151,123 @@ class HDRNerfactoInputDataset(InputDataset):
         return self._dataparser_outputs.image_filenames
 
 
+class HDRNerfactoWoCrfInputDataset(InputDataset):
+    """Dataset that returns HDR images, OpenEXR.
+
+    Args:
+        dataparser_outputs: description of where and how to read input images.
+        scale_factor: The scaling factor for the dataparser outputs
+    """
+
+    # exclude_batch_keys_from_device: List[str] = ["image", "mask"]
+    # cameras: Cameras
+
+    def __init__(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0):
+        super().__init__(dataparser_outputs, scale_factor)
+        
+    def __len__(self):
+        return len(self._dataparser_outputs.image_filenames)
+
+    def get_numpy_image(self, image_idx: int) -> npt.NDArray[np.uint8]:
+        """Returns the image of shape (H, W, 3 or 4), with np.float32 values.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        image_filename = self._dataparser_outputs.image_filenames[image_idx]
+        #print('image_filename', image_filename)
+        hdr_image = cv2.imread(str(image_filename),  cv2.IMREAD_UNCHANGED)
+        # print('hdr_image', hdr_image.shape, hdr_image.dtype)
+        # print('hdr_image', hdr_image.max(), hdr_image.min())
+        # print('hdr_image', hdr_image[0,0,:])
+        
+        if self.scale_factor != 1.0:
+            # Here cv2 img shape is (height, width, BGR)
+            height, width = hdr_image.shape[:2]
+            newsize = (int(width * self.scale_factor), int(height * self.scale_factor))
+            hdr_image = cv2.resize(hdr_image, newsize, interpolation = cv2.INTER_LINEAR)
+        hdr_image = cv2.cvtColor(hdr_image, cv2.COLOR_BGR2RGB) 
+        hdr_image = hdr_image.astype("float32") # shape is (h, w) or (h, w, 3 or 4)
+        if len(hdr_image.shape) == 2:
+            hdr_image = hdr_image[:, :, None].repeat(3, axis=2)
+        assert len(hdr_image.shape) == 3
+        assert hdr_image.dtype == np.float32
+        assert hdr_image.shape[2] in [3, 4], f"Image shape of {hdr_image.shape} is in correct."
+        return hdr_image
+
+    def get_image(self, image_idx: int) -> Float[Tensor, "image_height image_width num_channels"]:
+        """Returns a 3 channel image.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        image = torch.from_numpy(self.get_numpy_image(image_idx).astype("float32"))
+        #print('a', image.max())
+        # image = image * self._dataparser_outputs.exposures[image_idx]
+        # print('b', image.max())
+        
+        # if self._dataparser_outputs.alpha_color is not None and image.shape[-1] == 4:
+        #     image = image[:, :, :3] * image[:, :, -1:] + self._dataparser_outputs.alpha_color * (1.0 - image[:, :, -1:])
+        return image
+
+    def get_data(self, image_idx: int) -> Dict:
+        """Returns the ImageDataset data as a dictionary.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        image = self.get_image(image_idx)
+        data = {"image_idx": image_idx, "image": image}
+        
+         # TODO: this doesnt work for training
+        image_filename = self._dataparser_outputs.image_filenames[image_idx]
+        data["image_filename"] = str(image_filename.name)
+
+        if self._dataparser_outputs.mask_filenames is not None:
+            mask_filepath = self._dataparser_outputs.mask_filenames[image_idx]
+            data["mask"] = get_image_mask_tensor_from_path(filepath=mask_filepath, scale_factor=self.scale_factor)
+            assert (
+                data["mask"].shape[:2] == data["image"].shape[:2]
+            ), f"Mask and image have different shapes. Got {data['mask'].shape[:2]} and {data['image'].shape[:2]}"
+        
+        if self._dataparser_outputs.saturation_mask_filenames is not None:
+            saturation_mask_filepath = self._dataparser_outputs.saturation_mask_filenames[image_idx]
+            data["saturation_mask"] = get_image_mask_tensor_from_path(filepath=saturation_mask_filepath, scale_factor=self.scale_factor)
+            assert (
+                data["saturation_mask"].shape[:2] == data["image"].shape[:2]
+            ), f"Saturation mask and image have different shapes. Got {data['saturation_mask'].shape[:2]} and {data['image'].shape[:2]}"
+
+        if self._dataparser_outputs.exposures is not None:
+            exposure_value = self._dataparser_outputs.exposures[image_idx]
+            data["exposure"] = exposure_value
+        metadata = self.get_metadata(data)
+        data.update(metadata)
+        return data
+
+    def get_metadata(self, data: Dict) -> Dict:
+        """Method that can be used to process any additional metadata that may be part of the model inputs.
+
+        Args:
+            image_idx: The image index in the dataset.
+        """
+        del data
+        return {}
+
+    def __getitem__(self, image_idx: int) -> Dict:
+        data = self.get_data(image_idx)
+        return data
+
+    @property
+    def image_filenames(self) -> List[Path]:
+        """
+        Returns image filenames for this dataset.
+        The order of filenames is the same as in the Cameras object for easy mapping.
+        """
+
+        return self._dataparser_outputs.image_filenames
+
+
+
 class HDRInputDataset(InputDataset):
     """Dataset that returns HDR images, OpenEXR.