Update exporter.py to export sh_degree 0 case #3371

nerfstudio/models/splatfacto.py

@@ -314,7 +314,10 @@ def colors(self):
 
     @property
     def shs_0(self):
-        return self.features_dc
+        if self.config.sh_degree > 0:
+            return self.features_dc
+        else:
+            return RGB2SH(torch.sigmoid(self.features_dc))
 
     @property
     def shs_rest(self):

nerfstudio/scripts/exporter.py

@@ -485,6 +485,9 @@ class ExportGaussianSplat(Exporter):
     """Rotation of the oriented bounding box. Expressed as RPY Euler angles in radians"""
     obb_scale: Optional[Tuple[float, float, float]] = None
     """Scale of the oriented bounding box along each axis."""
+    ply_color_mode: Literal["sh_coeffs", "rgb"] = "sh_coeffs"
+    """If "rgb", export colors as red/green/blue fields. Otherwise, export colors as
+    spherical harmonics coefficients."""
 
     @staticmethod
     def write_ply(
@@ -504,7 +507,7 @@ def write_ply(
         """
 
         # Ensure count matches the length of all tensors
-        if not all(len(tensor) == count for tensor in map_to_tensors.values()):
+        if not all(tensor.size == count for tensor in map_to_tensors.values()):
             raise ValueError("Count does not match the length of all tensors")
 
         # Type check for numpy arrays of type float or uint8 and non-empty
@@ -552,7 +555,6 @@ def main(self) -> None:
 
         filename = self.output_dir / "splat.ply"
 
-        count = 0
         map_to_tensors = OrderedDict()
 
         with torch.no_grad():
@@ -566,19 +568,28 @@ def main(self) -> None:
             map_to_tensors["ny"] = np.zeros(n, dtype=np.float32)
             map_to_tensors["nz"] = np.zeros(n, dtype=np.float32)
 
-            if model.config.sh_degree > 0:
+            if self.ply_color_mode == "rgb":
+                colors = torch.clamp(model.colors.clone(), 0.0, 1.0).data.cpu().numpy()
+                colors = (colors * 255).astype(np.uint8)
+                map_to_tensors["red"] = colors[:, 0]
+                map_to_tensors["green"] = colors[:, 1]
+                map_to_tensors["blue"] = colors[:, 2]
+            elif self.ply_color_mode == "sh_coeffs":
                 shs_0 = model.shs_0.contiguous().cpu().numpy()
                 for i in range(shs_0.shape[1]):
                     map_to_tensors[f"f_dc_{i}"] = shs_0[:, i, None]
 
-                # transpose(1, 2) was needed to match the sh order in Inria version
-                shs_rest = model.shs_rest.transpose(1, 2).contiguous().cpu().numpy()
-                shs_rest = shs_rest.reshape((n, -1))
-                for i in range(shs_rest.shape[-1]):
-                    map_to_tensors[f"f_rest_{i}"] = shs_rest[:, i, None]
-            else:
-                colors = torch.clamp(model.colors.clone(), 0.0, 1.0).data.cpu().numpy()
-                map_to_tensors["colors"] = (colors * 255).astype(np.uint8)
+            if model.config.sh_degree > 0:
+                if self.ply_color_mode == "rgb":
+                    CONSOLE.print(
+                        "Warning: model has higher level of spherical harmonics, ignoring them and only export rgb."
+                    )
+                elif self.ply_color_mode == "sh_coeffs":
+                    # transpose(1, 2) was needed to match the sh order in Inria version
+                    shs_rest = model.shs_rest.transpose(1, 2).contiguous().cpu().numpy()
+                    shs_rest = shs_rest.reshape((n, -1))
+                    for i in range(shs_rest.shape[-1]):
+                        map_to_tensors[f"f_rest_{i}"] = shs_rest[:, i, None]
 
             map_to_tensors["opacity"] = model.opacities.data.cpu().numpy()