diff --git a/pcdet/models/backbones_3d/vfe/image_vfe.py b/pcdet/models/backbones_3d/vfe/image_vfe.py
new file mode 100644
index 0000000..f626986
--- /dev/null
+++ b/pcdet/models/backbones_3d/vfe/image_vfe.py
@@ -0,0 +1,85 @@
+import torch
+
+from .vfe_template import VFETemplate
+from .image_vfe_modules import ffn, f2v
+
+
+class ImageVFE(VFETemplate):
+    def __init__(self, model_cfg, grid_size, point_cloud_range, depth_downsample_factor, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+        self.grid_size = grid_size
+        self.pc_range = point_cloud_range
+        self.downsample_factor = depth_downsample_factor
+        self.module_topology = [
+            'ffn', 'f2v'
+        ]
+        self.build_modules()
+
+    def build_modules(self):
+        """
+        Builds modules
+        """
+        for module_name in self.module_topology:
+            module = getattr(self, 'build_%s' % module_name)()
+            self.add_module(module_name, module)
+
+    def build_ffn(self):
+        """
+        Builds frustum feature network
+        Returns:
+            ffn_module: nn.Module, Frustum feature network
+        """
+        ffn_module = ffn.__all__[self.model_cfg.FFN.NAME](
+            model_cfg=self.model_cfg.FFN,
+            downsample_factor=self.downsample_factor
+        )
+        self.disc_cfg = ffn_module.disc_cfg
+        return ffn_module
+
+    def build_f2v(self):
+        """
+        Builds frustum to voxel transformation
+        Returns:
+            f2v_module: nn.Module, Frustum to voxel transformation
+        """
+        f2v_module = f2v.__all__[self.model_cfg.F2V.NAME](
+            model_cfg=self.model_cfg.F2V,
+            grid_size=self.grid_size,
+            pc_range=self.pc_range,
+            disc_cfg=self.disc_cfg
+        )
+        return f2v_module
+
+    def get_output_feature_dim(self):
+        """
+        Gets number of output channels
+        Returns:
+            out_feature_dim: int, Number of output channels
+        """
+        out_feature_dim = self.ffn.get_output_feature_dim()
+        return out_feature_dim
+
+    def forward(self, batch_dict, **kwargs):
+        """
+        Args:
+            batch_dict:
+                images: (N, 3, H_in, W_in), Input images
+            **kwargs:
+        Returns:
+            batch_dict:
+                voxel_features: (B, C, Z, Y, X), Image voxel features
+        """
+        batch_dict = self.ffn(batch_dict)
+        batch_dict = self.f2v(batch_dict)
+        return batch_dict
+
+    def get_loss(self):
+        """
+        Gets DDN loss
+        Returns:
+            loss: (1), Depth distribution network loss
+            tb_dict: dict[float], All losses to log in tensorboard
+        """
+
+        loss, tb_dict = self.ffn.get_loss()
+        return loss, tb_dict