diff --git a/nerfstudio/model_components/renderers.py b/nerfstudio/model_components/renderers.py
index 6a8e2a5f9..d13c29233 100644
--- a/nerfstudio/model_components/renderers.py
+++ b/nerfstudio/model_components/renderers.py
@@ -341,37 +341,3 @@ class NormalsRenderer(nn.Module):
         if normalize:
             n = safe_normalize(n)
         return n
-
-
-class LambertianShadingRenderer(nn.Module):
-    """Calculate Lambertian shading."""
-
-    @classmethod
-    def forward(
-        cls,
-        rgb: TensorType["bs":..., 3],
-        normals: TensorType["bs":..., 3],
-        light_direction: TensorType["bs":..., 3],
-        shading_weight: float = 1.0,
-        detach_normals=True,
-    ):
-        """Calculate Lambertian shading.
-
-        Args:
-            rgb: Accumulated rgb along a ray.
-            normals: Accumulated normals along a ray.
-            light_direction: Direction of light source.
-            shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
-            detach_normals: Detach normals from the computation graph when computing shading.
-
-        Returns:
-            Textureless Lambertian shading, Lambertian shading
-        """
-        if detach_normals:
-            normals = normals.detach()
-
-        lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
-        shaded = lambertian.unsqueeze(-1).repeat(1, 3)
-        shaded_albedo = rgb * lambertian.unsqueeze(-1)
-
-        return shaded, shaded_albedo
diff --git a/nerfstudio/model_components/shaders.py b/nerfstudio/model_components/shaders.py
new file mode 100644
index 000000000..e71388e27
--- /dev/null
+++ b/nerfstudio/model_components/shaders.py
@@ -0,0 +1,77 @@
+# Copyright 2022 The Nerfstudio Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Shaders for rendering."""
+from typing import Optional
+
+from torch import nn
+from torchtyping import TensorType
+
+
+class LambertianShader(nn.Module):
+    """Calculate Lambertian shading."""
+
+    @classmethod
+    def forward(
+        cls,
+        rgb: TensorType["bs":..., 3],
+        normals: TensorType["bs":..., 3],
+        light_direction: TensorType["bs":..., 3],
+        shading_weight: float = 1.0,
+        detach_normals=True,
+    ):
+        """Calculate Lambertian shading.
+
+        Args:
+            rgb: Accumulated rgb along a ray.
+            normals: Accumulated normals along a ray.
+            light_direction: Direction of light source.
+            shading_weight: Lambertian shading (1.0) vs. ambient lighting (0.0) ratio
+            detach_normals: Detach normals from the computation graph when computing shading.
+
+        Returns:
+            Textureless Lambertian shading, Lambertian shading
+        """
+        if detach_normals:
+            normals = normals.detach()
+
+        lambertian = (1 - shading_weight) + shading_weight * (normals @ light_direction).clamp(min=0)
+        shaded = lambertian.unsqueeze(-1).repeat(1, 3)
+        shaded_albedo = rgb * lambertian.unsqueeze(-1)
+
+        return shaded, shaded_albedo
+
+
+class NormalsShader(nn.Module):
+    """Calculate shading for normals."""
+
+    @classmethod
+    def forward(
+        cls,
+        normals: TensorType["bs":..., 3],
+        weights: Optional[TensorType["bs":..., 1]] = None,
+    ):
+        """Applies a rainbow colormap to the normals.
+
+        Args:
+            normals: Normalized 3D vectors.
+            weights: Optional weights to scale to the normal colors. (Can be used for masking)
+
+        Returns:
+            Colored normals
+        """
+        normals = (normals + 1) / 2
+        if weights is not None:
+            normals = normals * weights
+        return normals
diff --git a/nerfstudio/models/nerfacto.py b/nerfstudio/models/nerfacto.py
index 91b044884..7442bfe8b 100644
--- a/nerfstudio/models/nerfacto.py
+++ b/nerfstudio/models/nerfacto.py
@@ -54,6 +54,7 @@ from nerfstudio.model_components.renderers import (
     RGBRenderer,
 )
 from nerfstudio.model_components.scene_colliders import NearFarCollider
+from nerfstudio.model_components.shaders import NormalsShader
 from nerfstudio.models.base_model import Model, ModelConfig
 from nerfstudio.utils import colormaps
 
@@ -187,6 +188,9 @@ class NerfactoModel(Model):
         self.renderer_depth = DepthRenderer()
         self.renderer_normals = NormalsRenderer()
 
+        # shaders
+        self.normals_shader = NormalsShader()
+
         # losses
         self.rgb_loss = MSELoss()
 
@@ -252,9 +256,8 @@ class NerfactoModel(Model):
         if self.config.predict_normals:
             normals = self.renderer_normals(normals=field_outputs[FieldHeadNames.NORMALS], weights=weights)
             pred_normals = self.renderer_normals(field_outputs[FieldHeadNames.PRED_NORMALS], weights=weights)
-            outputs["normals"] = (normals + 1) / 2
-            outputs["pred_normals"] = (pred_normals + 1) / 2
-
+            outputs["normals"] = self.normals_shader(normals)
+            outputs["pred_normals"] = self.normals_shader(pred_normals)
         # These use a lot of GPU memory, so we avoid storing them for eval.
         if self.training:
             outputs["weights_list"] = weights_list
@@ -336,12 +339,6 @@ class NerfactoModel(Model):
 
         images_dict = {"img": combined_rgb, "accumulation": combined_acc, "depth": combined_depth}
 
-        # normals to RGB for visualization. TODO: use a colormap
-        if "normals" in outputs:
-            images_dict["normals"] = (outputs["normals"] + 1.0) / 2.0
-        if "pred_normals" in outputs:
-            images_dict["pred_normals"] = (outputs["pred_normals"] + 1.0) / 2.0
-
         for i in range(self.config.num_proposal_iterations):
             key = f"prop_depth_{i}"
             prop_depth_i = colormaps.apply_depth_colormap(