diff --git a/nerfstudio/configs/method_configs.py b/nerfstudio/configs/method_configs.py
index 947dad880..f5eec1086 100644
--- a/nerfstudio/configs/method_configs.py
+++ b/nerfstudio/configs/method_configs.py
@@ -83,7 +83,7 @@ method_configs["nerfacto"] = TrainerConfig(
     pipeline=VanillaPipelineConfig(
         datamanager=VanillaDataManagerConfig(
             dataparser=NerfstudioDataParserConfig(),
-            train_num_rays_per_batch=4096,
+            train_num_rays_per_batch=8192,
             eval_num_rays_per_batch=4096,
             camera_optimizer=CameraOptimizerConfig(
                 mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
diff --git a/nerfstudio/data/datamanagers/base_datamanager.py b/nerfstudio/data/datamanagers/base_datamanager.py
index 8114cf6cb..e9dd0f3fe 100644
--- a/nerfstudio/data/datamanagers/base_datamanager.py
+++ b/nerfstudio/data/datamanagers/base_datamanager.py
@@ -351,7 +351,7 @@ class VanillaDataManager(DataManager):  # pylint: disable=abstract-method
         # Otherwise, use the default pixel sampler
         if is_equirectangular.any():
             CONSOLE.print("[bold yellow]Warning: Some cameras are equirectangular, but using default pixel sampler.")
-        return PatchPixelSampler(*args, **kwargs,patch_size=3)
+        return PatchPixelSampler(*args, **kwargs,patch_size=16)
 
     def setup_train(self):
         """Sets up the data loaders for training"""
diff --git a/nerfstudio/model_components/losses.py b/nerfstudio/model_components/losses.py
index 6a981d664..8473c46ce 100644
--- a/nerfstudio/model_components/losses.py
+++ b/nerfstudio/model_components/losses.py
@@ -302,3 +302,23 @@ def depth_loss(
         return urban_radiance_field_depth_loss(weights, termination_depth, predicted_depth, steps, sigma)
 
     raise NotImplementedError("Provided depth loss type not implemented.")
+
+def robust_rgb_loss(rgb_outputs,rgb_gt,patch_size=16):
+    out_patches = rgb_outputs.view(-1,1,patch_size,patch_size)
+    gt_patches = rgb_gt.view(-1,1,patch_size,patch_size)
+    device = rgb_outputs.device
+    batch_size = out_patches.shape[0]
+    residuals = (out_patches - gt_patches)**2
+
+    with torch.no_grad():
+        med_residual = torch.median(residuals)
+        #equation 8
+        weight = (residuals<med_residual).float()
+        #equation 9
+        blurred_w = (torch.nn.functional.conv2d(weight,(1/9.)*torch.ones((1,1,3,3),device=device),padding='same')>0.5).float()
+        expected_w = torch.mean(torch.mean(blurred_w,dim=-1),dim=-1)
+        weight_r8 = (expected_w > 0.6).float()
+        w_8x8 = weight_r8[...,None] * torch.ones((batch_size,8,8),device=device)
+        final_w = torch.zeros((batch_size,16,16),device=device)
+        final_w[:,4:12,4:12] = w_8x8
+    return torch.mean(residuals*final_w)
\ No newline at end of file
diff --git a/nerfstudio/models/nerfacto.py b/nerfstudio/models/nerfacto.py
index a0938455a..f4204840d 100644
--- a/nerfstudio/models/nerfacto.py
+++ b/nerfstudio/models/nerfacto.py
@@ -45,6 +45,7 @@ from nerfstudio.model_components.losses import (
     interlevel_loss,
     orientation_loss,
     pred_normal_loss,
+    robust_rgb_loss
 )
 from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler
 from nerfstudio.model_components.renderers import (
@@ -303,7 +304,7 @@ class NerfactoModel(Model):
     def get_loss_dict(self, outputs, batch, metrics_dict=None):
         loss_dict = {}
         image = batch["image"].to(self.device)
-        loss_dict["rgb_loss"] = self.rgb_loss(image, outputs["rgb"])
+        loss_dict["rgb_loss"] = robust_rgb_loss(image, outputs["rgb"])
         if self.training:
             loss_dict["interlevel_loss"] = self.config.interlevel_loss_mult * interlevel_loss(
                 outputs["weights_list"], outputs["ray_samples_list"]
@@ -320,10 +321,6 @@ class NerfactoModel(Model):
                 loss_dict["pred_normal_loss"] = self.config.pred_normal_loss_mult * torch.mean(
                     outputs["rendered_pred_normal_loss"]
                 )
-            #depth regularity loss
-            p=3
-            patches = outputs['depth'].view(-1,p,p) #Bx3x3
-            loss_dict['depth_reg'] = np.interp(self.step,[0,500],[0,1])*torch.mean(torch.sum((patches.view(-1,p*p) - patches[:,p//2,p//2][:,None])**2,dim=1))
 
         return loss_dict