diff --git a/nerfstudio/cameras/camera_optimizers.py b/nerfstudio/cameras/camera_optimizers.py
index 6ab8cc2a0..9b47b7e53 100644
--- a/nerfstudio/cameras/camera_optimizers.py
+++ b/nerfstudio/cameras/camera_optimizers.py
@@ -22,6 +22,7 @@ import functools
 from dataclasses import dataclass, field
 from typing import Literal, Optional, Type, Union
 
+import numpy
 import torch
 import tyro
 from jaxtyping import Float, Int
@@ -151,15 +152,20 @@ class CameraOptimizer(nn.Module):
             raybundle.origins = raybundle.origins + correction_matrices[:, :3, 3]
             raybundle.directions = torch.bmm(correction_matrices[:, :3, :3], raybundle.directions[..., None]).squeeze()
 
-    def apply_to_camera(self, camera: Cameras) -> None:
-        """Apply the pose correction to the raybundle"""
-        if self.config.mode != "off":
-            assert camera.metadata is not None, "Must provide id of camera in its metadata"
-            assert "cam_idx" in camera.metadata, "Must provide id of camera in its metadata"
-            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)
-            camera.camera_to_worlds = torch.bmm(camera.camera_to_worlds, adj)
+    def apply_to_camera(self, camera: Cameras) -> torch.Tensor:
+        """Apply the pose correction to the world-to-camera matrix in a Camera object"""
+        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?
+            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)
 
     def get_loss_dict(self, loss_dict: dict) -> None:
         """Add regularization"""
@@ -176,8 +182,12 @@ class CameraOptimizer(nn.Module):
     def get_metrics_dict(self, metrics_dict: dict) -> None:
         """Get camera optimizer metrics"""
         if self.config.mode != "off":
-            metrics_dict["camera_opt_translation"] = self.pose_adjustment[:, :3].norm()
-            metrics_dict["camera_opt_rotation"] = self.pose_adjustment[:, 3:].norm()
+            trans = self.pose_adjustment[:, :3].detach().norm(dim=-1)
+            rot = self.pose_adjustment[:, 3:].detach().norm(dim=-1)
+            metrics_dict["camera_opt_translation_max"] = trans.max()
+            metrics_dict["camera_opt_translation_mean"] = trans.mean()
+            metrics_dict["camera_opt_rotation_mean"] = numpy.rad2deg(rot.mean().cpu())
+            metrics_dict["camera_opt_rotation_max"] = numpy.rad2deg(rot.max().cpu())
 
     def get_param_groups(self, param_groups: dict) -> None:
         """Get camera optimizer parameters"""
diff --git a/nerfstudio/configs/method_configs.py b/nerfstudio/configs/method_configs.py
index a78823995..cbbec0d04 100644
--- a/nerfstudio/configs/method_configs.py
+++ b/nerfstudio/configs/method_configs.py
@@ -632,8 +632,10 @@ method_configs["splatfacto"] = TrainerConfig(
         },
         "quats": {"optimizer": AdamOptimizerConfig(lr=0.001, eps=1e-15), "scheduler": None},
         "camera_opt": {
-            "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=5e-5, max_steps=30000),
+            "optimizer": AdamOptimizerConfig(lr=1e-4, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(
+                lr_final=5e-7, max_steps=30000, warmup_steps=1000, lr_pre_warmup=0
+            ),
         },
     },
     viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
@@ -684,8 +686,10 @@ method_configs["splatfacto-big"] = TrainerConfig(
         },
         "quats": {"optimizer": AdamOptimizerConfig(lr=0.001, eps=1e-15), "scheduler": None},
         "camera_opt": {
-            "optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
-            "scheduler": ExponentialDecaySchedulerConfig(lr_final=5e-5, max_steps=30000),
+            "optimizer": AdamOptimizerConfig(lr=1e-4, eps=1e-15),
+            "scheduler": ExponentialDecaySchedulerConfig(
+                lr_final=5e-7, max_steps=30000, warmup_steps=1000, lr_pre_warmup=0
+            ),
         },
     },
     viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
diff --git a/nerfstudio/models/splatfacto.py b/nerfstudio/models/splatfacto.py
index 7d7a78d2d..0aebc63b4 100644
--- a/nerfstudio/models/splatfacto.py
+++ b/nerfstudio/models/splatfacto.py
@@ -33,6 +33,7 @@ from pytorch_msssim import SSIM
 from torch.nn import Parameter
 from typing_extensions import Literal
 
+from nerfstudio.cameras.camera_optimizers import CameraOptimizer, CameraOptimizerConfig
 from nerfstudio.cameras.cameras import Cameras
 from nerfstudio.data.scene_box import OrientedBox
 from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes, TrainingCallbackLocation
@@ -146,6 +147,8 @@ class SplatfactoModelConfig(ModelConfig):
     However, PLY exported with antialiased rasterize mode is not compatible with classic mode. Thus many web viewers that
     were implemented for classic mode can not render antialiased mode PLY properly without modifications.
     """
+    camera_optimizer: CameraOptimizerConfig = field(default_factory=lambda: CameraOptimizerConfig(mode="off"))
+    """Config of the camera optimizer to use"""
 
 
 class SplatfactoModel(Model):
@@ -213,6 +216,10 @@ class SplatfactoModel(Model):
             }
         )
 
+        self.camera_optimizer: CameraOptimizer = self.config.camera_optimizer.setup(
+            num_cameras=self.num_train_data, device="cpu"
+        )
+
         # metrics
         from torchmetrics.image import PeakSignalNoiseRatio
         from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
@@ -609,6 +616,7 @@ class SplatfactoModel(Model):
             Mapping of different parameter groups
         """
         gps = self.get_gaussian_param_groups()
+        self.camera_optimizer.get_param_groups(param_groups=gps)
         return gps
 
     def _get_downscale_factor(self):
@@ -648,6 +656,8 @@ class SplatfactoModel(Model):
 
         # 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":
@@ -657,6 +667,8 @@ class SplatfactoModel(Model):
             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:
@@ -674,8 +686,9 @@ class SplatfactoModel(Model):
         camera_downscale = self._get_downscale_factor()
         camera.rescale_output_resolution(1 / camera_downscale)
         # shift the camera to center of scene looking at center
-        R = camera.camera_to_worlds[0, :3, :3]  # 3 x 3
-        T = camera.camera_to_worlds[0, :3, 3:4]  # 3 x 1
+        R = optimized_camera_to_world[:3, :3]  # 3 x 3
+        T = optimized_camera_to_world[:3, 3:4]  # 3 x 1
+
         # flip the z and y axes to align with gsplat conventions
         R_edit = torch.diag(torch.tensor([1, -1, -1], device=self.device, dtype=R.dtype))
         R = R @ R_edit
@@ -738,7 +751,7 @@ class SplatfactoModel(Model):
             self.xys.retain_grad()
 
         if self.config.sh_degree > 0:
-            viewdirs = means_crop.detach() - camera.camera_to_worlds.detach()[..., :3, 3]  # (N, 3)
+            viewdirs = means_crop.detach() - optimized_camera_to_world.detach()[:3, 3]  # (N, 3)
             viewdirs = viewdirs / viewdirs.norm(dim=-1, keepdim=True)
             n = min(self.step // self.config.sh_degree_interval, self.config.sh_degree)
             rgbs = spherical_harmonics(n, viewdirs, colors_crop)
@@ -829,6 +842,8 @@ class SplatfactoModel(Model):
         metrics_dict["psnr"] = self.psnr(predicted_rgb, gt_rgb)
 
         metrics_dict["gaussian_count"] = self.num_points
+
+        self.camera_optimizer.get_metrics_dict(metrics_dict)
         return metrics_dict
 
     def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Tensor]:
@@ -867,11 +882,17 @@ class SplatfactoModel(Model):
         else:
             scale_reg = torch.tensor(0.0).to(self.device)
 
-        return {
+        loss_dict = {
             "main_loss": (1 - self.config.ssim_lambda) * Ll1 + self.config.ssim_lambda * simloss,
             "scale_reg": scale_reg,
         }
 
+        if self.training:
+            # Add loss from camera optimizer
+            self.camera_optimizer.get_loss_dict(loss_dict)
+
+        return loss_dict
+
     @torch.no_grad()
     def get_outputs_for_camera(self, camera: Cameras, obb_box: Optional[OrientedBox] = None) -> Dict[str, torch.Tensor]:
         """Takes in a camera, generates the raybundle, and computes the output of the model.