diff --git a/nerfstudio/data/pixel_samplers.py b/nerfstudio/data/pixel_samplers.py
index f2bc6d96ef..d4ca82d030 100644
--- a/nerfstudio/data/pixel_samplers.py
+++ b/nerfstudio/data/pixel_samplers.py
@@ -18,6 +18,7 @@
 
 import random
 import warnings
+from collections import defaultdict
 from dataclasses import dataclass, field
 from typing import Dict, Optional, Type, Union
 
@@ -335,8 +336,7 @@ def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int,
 
         # only sample within the mask, if the mask is in the batch
         all_indices = []
-        all_images = []
-        all_depth_images = []
+        all_images = defaultdict(list)
 
         assert num_rays_per_batch % 2 == 0, "num_rays_per_batch must be divisible by 2"
         num_rays_per_image = divide_rays_per_image(num_rays_per_batch, num_images)
@@ -350,10 +350,11 @@ def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int,
                 )
                 indices[:, 0] = i
                 all_indices.append(indices)
-                all_images.append(batch["image"][i][indices[:, 1], indices[:, 2]])
-                if "depth_image" in batch:
-                    all_depth_images.append(batch["depth_image"][i][indices[:, 1], indices[:, 2]])
 
+                for key, value in batch.items():
+                    if key in ["image_idx", "mask"]:
+                        continue
+                    all_images[key].append(value[i][indices[:, 1], indices[:, 2]])
         else:
             for i, num_rays in enumerate(num_rays_per_image):
                 image_height, image_width, _ = batch["image"][i].shape
@@ -363,26 +364,19 @@ def collate_image_dataset_batch_list(self, batch: Dict, num_rays_per_batch: int,
                     indices = self.sample_method(num_rays, 1, image_height, image_width, device=device)
                 indices[:, 0] = i
                 all_indices.append(indices)
-                all_images.append(batch["image"][i][indices[:, 1], indices[:, 2]])
-                if "depth_image" in batch:
-                    all_depth_images.append(batch["depth_image"][i][indices[:, 1], indices[:, 2]])
+                for key, value in batch.items():
+                    if key in ["image_idx", "mask"]:
+                        continue
+                    all_images[key].append(value[i][indices[:, 1], indices[:, 2]])
 
         indices = torch.cat(all_indices, dim=0)
 
-        c, y, x = (i.flatten() for i in torch.split(indices, 1, dim=-1))
-        collated_batch = {
-            key: value[c, y, x]
-            for key, value in batch.items()
-            if key not in ("image_idx", "image", "mask", "depth_image") and value is not None
-        }
-
-        collated_batch["image"] = torch.cat(all_images, dim=0)
-        if "depth_image" in batch:
-            collated_batch["depth_image"] = torch.cat(all_depth_images, dim=0)
+        collated_batch = {key: torch.cat(all_images[key], dim=0) for key in all_images}
 
         assert collated_batch["image"].shape[0] == num_rays_per_batch
 
         # Needed to correct the random indices to their actual camera idx locations.
+        c = indices[..., 0].flatten()
         indices[:, 0] = batch["image_idx"][c]
         collated_batch["indices"] = indices  # with the abs camera indices