fix the bugs associated with blender datas

nerfstudio/data/datamanagers/full_images_datamanager.py

@@ -104,7 +104,7 @@ def __init__(
         self.train_dataset = self.create_train_dataset()
         self.eval_dataset = self.create_eval_dataset()
         if len(self.train_dataset) > 500 and self.config.cache_images == "gpu":
-            CONSOLE.print("Train dataset has over 500 images, overriding cach_images to cpu", style="bold yellow")
+            CONSOLE.print("Train dataset has over 500 images, overriding cache_images to cpu", style="bold yellow")
             self.config.cache_images = "cpu"
         self.cached_train, self.cached_eval = self.cache_images(self.config.cache_images)
         self.exclude_batch_keys_from_device = self.train_dataset.exclude_batch_keys_from_device
@@ -130,6 +130,7 @@ def cache_images(self, cache_images_option):
             camera = self.train_dataset.cameras[i].reshape(())
             K = camera.get_intrinsics_matrices().numpy()
             if camera.distortion_params is None:
+                cached_train.append(data)
                 continue
             distortion_params = camera.distortion_params.numpy()
             image = data["image"].numpy()
@@ -205,6 +206,7 @@ def cache_images(self, cache_images_option):
             camera = self.eval_dataset.cameras[i].reshape(())
             K = camera.get_intrinsics_matrices().numpy()
             if camera.distortion_params is None:
+                cached_eval.append(data)
                 continue
             distortion_params = camera.distortion_params.numpy()
             image = data["image"].numpy()

nerfstudio/data/dataparsers/blender_dataparser.py

@@ -28,6 +28,7 @@
 from nerfstudio.data.scene_box import SceneBox
 from nerfstudio.utils.colors import get_color
 from nerfstudio.utils.io import load_from_json
+from nerfstudio.model_components.renderers import force_background_color_override
 
 
 @dataclass
@@ -57,13 +58,13 @@ def __init__(self, config: BlenderDataParserConfig):
         self.data: Path = config.data
         self.scale_factor: float = config.scale_factor
         self.alpha_color = config.alpha_color
-
-    def _generate_dataparser_outputs(self, split="train"):
         if self.alpha_color is not None:
-            alpha_color_tensor = get_color(self.alpha_color)
+            self.alpha_color_tensor = get_color(self.alpha_color)
+            force_background_color_override(self.alpha_color_tensor)
         else:
-            alpha_color_tensor = None
+            self.alpha_color_tensor = None
 
+    def _generate_dataparser_outputs(self, split="train"):
         meta = load_from_json(self.data / f"transforms_{split}.json")
         image_filenames = []
         poses = []
@@ -98,7 +99,7 @@ def _generate_dataparser_outputs(self, split="train"):
         dataparser_outputs = DataparserOutputs(
             image_filenames=image_filenames,
             cameras=cameras,
-            alpha_color=alpha_color_tensor,
+            alpha_color=self.alpha_color_tensor,
             scene_box=scene_box,
             dataparser_scale=self.scale_factor,
         )

nerfstudio/model_components/renderers.py

@@ -55,6 +55,12 @@ def background_color_override_context(mode: Float[Tensor, "3"]) -> Generator[Non
         BACKGROUND_COLOR_OVERRIDE = old_background_color
 
 
+def force_background_color_override(mode: Float[Tensor, "3"]) -> None:
+    """Force background color override."""
+    global BACKGROUND_COLOR_OVERRIDE
+    BACKGROUND_COLOR_OVERRIDE = mode
+
+
 class RGBRenderer(nn.Module):
     """Standard volumetric rendering.
 

nerfstudio/models/gaussian_splatting.py

@@ -596,14 +596,15 @@ def get_outputs(self, camera: Cameras) -> Dict[str, Union[torch.Tensor, List]]:
         if self.training:
             # currently relies on the branch vickie/camera-grads
             self.camera_optimizer.apply_to_camera(camera)
-        if self.training:
-            background = torch.rand(3, device=self.device)
+        # get the background color
+        if renderers.BACKGROUND_COLOR_OVERRIDE is not None:
+            background = renderers.BACKGROUND_COLOR_OVERRIDE.to(self.device)
         else:
-            # logic for setting the background of the scene
-            if renderers.BACKGROUND_COLOR_OVERRIDE is not None:
-                background = renderers.BACKGROUND_COLOR_OVERRIDE
+            if self.training:
+                background = torch.rand(3, device=self.device)
             else:
                 background = self.back_color.to(self.device)
+
         if self.crop_box is not None and not self.training:
             crop_ids = self.crop_box.within(self.means).squeeze()
             if crop_ids.sum() == 0: