Merge branch 'main' into main

nerfstudio/cameras/camera_optimizers.py

@@ -157,15 +157,24 @@ def apply_to_camera(self, camera: Cameras) -> torch.Tensor:
         if self.config.mode == "off":
             return camera.camera_to_worlds
 
-        assert camera.metadata is not None, "Must provide id of camera in its metadata"
-        if "cam_idx" not in camera.metadata:
-            # Evalutaion cams?
+        if camera.metadata is None or "cam_idx" not in camera.metadata:
+            # Viser cameras
             return camera.camera_to_worlds
 
         camera_idx = camera.metadata["cam_idx"]
-        adj = self(torch.tensor([camera_idx], dtype=torch.long, device=camera.device))  # type: ignore
-        adj = torch.cat([adj, torch.Tensor([0, 0, 0, 1])[None, None].to(adj)], dim=1)
-        return torch.bmm(camera.camera_to_worlds, adj)
+        adj = self(torch.tensor([camera_idx], dtype=torch.long)).to(camera.device)  # type: ignore
+
+        return torch.cat(
+            [
+                # Apply rotation to directions in world coordinates, without touching the origin.
+                # Equivalent to: directions -> correction[:3,:3] @ directions
+                torch.bmm(adj[..., :3, :3], camera.camera_to_worlds[..., :3, :3]),
+                # Apply translation in world coordinate, independently of rotation.
+                # Equivalent to: origins -> origins + correction[:3,3]
+                camera.camera_to_worlds[..., :3, 3:] + adj[..., :3, 3:],
+            ],
+            dim=-1,
+        )
 
     def get_loss_dict(self, loss_dict: dict) -> None:
         """Add regularization"""

nerfstudio/exporter/exporter_utils.py

@@ -30,6 +30,7 @@
 from rich.progress import BarColumn, Progress, TaskProgressColumn, TextColumn, TimeRemainingColumn
 from torch import Tensor
 
+from nerfstudio.cameras.camera_optimizers import CameraOptimizer
 from nerfstudio.cameras.cameras import Cameras
 from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.data.datasets.base_dataset import InputDataset
@@ -283,11 +284,14 @@ def render_trajectory(
     return images, depths
 
 
-def collect_camera_poses_for_dataset(dataset: Optional[InputDataset]) -> List[Dict[str, Any]]:
+def collect_camera_poses_for_dataset(
+    dataset: Optional[InputDataset], camera_optimizer: Optional[CameraOptimizer] = None
+) -> List[Dict[str, Any]]:
     """Collects rescaled, translated and optimised camera poses for a dataset.
 
     Args:
         dataset: Dataset to collect camera poses for.
+        camera_optimizer: Camera optimizer that has been used for adjusting the poses
 
     Returns:
         List of dicts containing camera poses.
@@ -304,7 +308,15 @@ def collect_camera_poses_for_dataset(dataset: Optional[InputDataset]) -> List[Di
     # new cameras are in cameras, whereas image paths are stored in a private member of the dataset
     for idx in range(len(cameras)):
         image_filename = image_filenames[idx]
-        transform = cameras.camera_to_worlds[idx].tolist()
+        if camera_optimizer is None:
+            transform = cameras.camera_to_worlds[idx].tolist()
+        else:
+            # print('exporting optimized camera pose for camera %d' % idx)
+            camera = cameras[idx : idx + 1]
+            assert camera.metadata is not None
+            camera.metadata["cam_idx"] = idx
+            transform = camera_optimizer.apply_to_camera(camera).tolist()[0]
+
         frames.append(
             {
                 "file_path": str(image_filename),
@@ -331,7 +343,12 @@ def collect_camera_poses(pipeline: VanillaPipeline) -> Tuple[List[Dict[str, Any]
     eval_dataset = pipeline.datamanager.eval_dataset
     assert isinstance(eval_dataset, InputDataset)
 
-    train_frames = collect_camera_poses_for_dataset(train_dataset)
+    camera_optimizer = None
+    if hasattr(pipeline.model, "camera_optimizer"):
+        camera_optimizer = pipeline.model.camera_optimizer
+
+    train_frames = collect_camera_poses_for_dataset(train_dataset, camera_optimizer)
+    # Note: returning original poses, even if --eval-mode=all
     eval_frames = collect_camera_poses_for_dataset(eval_dataset)
 
     return train_frames, eval_frames

nerfstudio/models/splatfacto.py

@@ -672,10 +672,10 @@ def get_outputs(self, camera: Cameras) -> Dict[str, Union[torch.Tensor, List]]:
             return {}
         assert camera.shape[0] == 1, "Only one camera at a time"
 
+        optimized_camera_to_world = self.camera_optimizer.apply_to_camera(camera)[0, ...]
+
         # get the background color
         if self.training:
-            optimized_camera_to_world = self.camera_optimizer.apply_to_camera(camera)[0, ...]
-
             if self.config.background_color == "random":
                 background = torch.rand(3, device=self.device)
             elif self.config.background_color == "white":
@@ -685,8 +685,6 @@ def get_outputs(self, camera: Cameras) -> Dict[str, Union[torch.Tensor, List]]:
             else:
                 background = self.background_color.to(self.device)
         else:
-            optimized_camera_to_world = camera.camera_to_worlds[0, ...]
-
             if renderers.BACKGROUND_COLOR_OVERRIDE is not None:
                 background = renderers.BACKGROUND_COLOR_OVERRIDE.to(self.device)
             else: