diff --git a/nerfstudio/models/gaussian_splatting.py b/nerfstudio/models/gaussian_splatting.py
index 01983d30f..331a45df8 100644
--- a/nerfstudio/models/gaussian_splatting.py
+++ b/nerfstudio/models/gaussian_splatting.py
@@ -66,6 +66,9 @@ def RGB2SH(rgb):
     C0 = 0.28209479177387814
     return (rgb - 0.5) / C0
 
+def SH2RGB(sh):
+    C0 = 0.28209479177387814
+    return sh * C0 + 0.5
 
 def projection_matrix(znear, zfar, fovx, fovy, device="cpu"):
     t = znear * math.tan(0.5 * fovy)
@@ -196,8 +199,12 @@ class GaussianSplattingModel(Model):
         )
 
     @property
-    def get_colors(self):
-        return self.colors_all
+    def colors(self):
+        return SH2RGB(self.colors_all[:, 0, :])
+    
+    @property
+    def shs_rest(self):
+        return self.colors_all[:, 1:, :]
 
     def load_state_dict(self, dict, **kwargs):
         # resize the parameters to match the new number of points
@@ -546,13 +553,13 @@ class GaussianSplattingModel(Model):
         if crop_ids is not None:
             opacities_crop = self.opacities[crop_ids]
             means_crop = self.means[crop_ids]
-            colors_crop = self.get_colors[crop_ids]
+            colors_crop = self.colors_all[crop_ids]
             scales_crop = self.scales[crop_ids]
             quats_crop = self.quats[crop_ids]
         else:
             opacities_crop = self.opacities
             means_crop = self.means
-            colors_crop = self.get_colors
+            colors_crop = self.colors_all
             scales_crop = self.scales
             quats_crop = self.quats
         self.xys, depths, self.radii, conics, num_tiles_hit, cov3d = ProjectGaussians.apply(
diff --git a/nerfstudio/scripts/exporter.py b/nerfstudio/scripts/exporter.py
index 821dd952e..028cddd31 100644
--- a/nerfstudio/scripts/exporter.py
+++ b/nerfstudio/scripts/exporter.py
@@ -502,11 +502,12 @@ class ExportGaussianSplat(Exporter):
             map_to_tensors["normals"] = o3d.core.Tensor(np.zeros_like(positions), o3d.core.float32)
 
             colors = model.colors.data.cpu().numpy()
+            map_to_tensors["colors"] = (colors * 255).astype(np.uint8)
             for i in range(colors.shape[1]):
-                map_to_tensors[f"f_dc_{i}"] = colors[:, i]
+                map_to_tensors[f"f_dc_{i}"] = colors[:, i:i+1]
 
             shs = model.shs_rest.data.cpu().numpy()
-            if shs.size > 0:
+            if model.config.sh_degree > 0:
                 shs = shs.reshape((colors.shape[0], -1, 1))
                 for i in range(shs.shape[-1]):
                     map_to_tensors[f"f_rest_{i}"] = shs[:, i]