diff --git a/nerfstudio/process_data/images_to_nerfstudio_dataset.py b/nerfstudio/process_data/images_to_nerfstudio_dataset.py
index 7ae7d5bd4..fa2d8b2bc 100644
--- a/nerfstudio/process_data/images_to_nerfstudio_dataset.py
+++ b/nerfstudio/process_data/images_to_nerfstudio_dataset.py
@@ -87,7 +87,9 @@ class ImagesToNerfstudioDataset(ColmapConverterToNerfstudioDataset):
                 )
                 image_rename_map_paths.update(eval_image_rename_map_paths)
 
-            image_rename_map = dict((a.name, b.name) for a, b in image_rename_map_paths.items())
+            image_rename_map = dict(
+                (a.relative_to(self.data).as_posix(), b.name) for a, b in image_rename_map_paths.items()
+            )
             num_frames = len(image_rename_map)
             summary_log.append(f"Starting with {num_frames} images")
 
diff --git a/nerfstudio/process_data/process_data_utils.py b/nerfstudio/process_data/process_data_utils.py
index d754525d3..b5b2391a0 100644
--- a/nerfstudio/process_data/process_data_utils.py
+++ b/nerfstudio/process_data/process_data_utils.py
@@ -62,7 +62,7 @@ CAMERA_MODELS = {
 }
 
 
-def list_images(data: Path, recursive: bool = False) -> List[Path]:
+def list_images(data: Path, recursive: bool = True) -> List[Path]:
     """Lists all supported images in a directory
 
     Args:
diff --git a/tests/process_data/test_process_images.py b/tests/process_data/test_process_images.py
index 8482676c5..fd506b1fb 100644
--- a/tests/process_data/test_process_images.py
+++ b/tests/process_data/test_process_images.py
@@ -121,3 +121,90 @@ def test_process_images_skip_colmap(tmp_path: Path):
     )
     dataparser_poses = np.linalg.inv(dataparser_poses)
     np.testing.assert_allclose(gt_poses, dataparser_poses, rtol=0, atol=1e-5)
+
+
+def test_process_images_recursively_skip_colmap(tmp_path: Path):
+    """
+    Test ns-process-data images when images contains subdirectories"
+    """
+    # Mock a colmap sparse model
+    width = 100
+    height = 150
+    sparse_path = tmp_path / "sparse" / "0"
+    sparse_path.mkdir(exist_ok=True, parents=True)
+    (tmp_path / "images").mkdir(exist_ok=True, parents=True)
+    write_cameras_binary(
+        {1: Camera(1, "OPENCV", width, height, [110, 110, 50, 75, 0, 0, 0, 0, 0, 0])},
+        sparse_path / "cameras.bin",
+    )
+    write_points3D_binary(
+        {
+            1: Point3D(
+                id=1,
+                xyz=np.array([0, 0, 0]),
+                rgb=np.array([0, 0, 0]),
+                error=np.array([0]),
+                image_ids=np.array([1]),
+                point2D_idxs=np.array([0]),
+            ),
+        },
+        sparse_path / "points3D.bin",
+    )
+    frames = {}
+    num_frames = 9
+    num_subdirs = 3
+    qvecs = random_quaternion(num_frames)
+    tvecs = np.random.uniform(size=(num_frames, 3))
+    original_poses = np.concatenate(
+        (
+            np.concatenate(
+                (
+                    np.stack(list(map(qvec2rotmat, qvecs))),
+                    tvecs[:, :, None],
+                ),
+                -1,
+            ),
+            np.array([[[0, 0, 0, 1]]], dtype=qvecs.dtype).repeat(num_frames, 0),
+        ),
+        -2,
+    )
+    for i in range(num_frames):
+        subdir = f"subdir_{num_frames // num_subdirs}"
+        frames[i + 1] = ColmapImage(i + 1, qvecs[i], tvecs[i], 1, f"{subdir}/image_{i}.png", [], [])
+        (tmp_path / "images" / subdir).mkdir(parents=True, exist_ok=True)
+        Image.new("RGB", (width, height)).save(tmp_path / "images" / subdir / f"image_{i}.png")
+    write_images_binary(frames, sparse_path / "images.bin")
+
+    # Mock missing COLMAP and ffmpeg in the dev env
+    old_path = os.environ.get("PATH", "")
+    os.environ["PATH"] = str(tmp_path / "mocked_bin") + f":{old_path}"
+    (tmp_path / "mocked_bin").mkdir()
+    (tmp_path / "mocked_bin" / "colmap").touch(mode=0o777)
+    (tmp_path / "mocked_bin" / "ffmpeg").touch(mode=0o777)
+
+    # Convert images into a NerfStudio dataset
+    cmd = ImagesToNerfstudioDataset(
+        data=tmp_path / "images", output_dir=tmp_path / "nerfstudio", colmap_model_path=sparse_path, skip_colmap=True
+    )
+    cmd.main()
+    os.environ["PATH"] = old_path
+
+    assert (tmp_path / "nerfstudio" / "transforms.json").exists()
+    parser = NerfstudioDataParserConfig(
+        data=tmp_path / "nerfstudio",
+        downscale_factor=None,
+        orientation_method="none",  # orientation_method,
+        center_method="none",
+        auto_scale_poses=False,
+    ).setup()
+    outputs = parser.get_dataparser_outputs("train")
+    assert len(outputs.image_filenames) == 9
+    assert torch.is_tensor(outputs.dataparser_transform)
+
+    # Test if the original poses can be obtained back
+    dataparser_poses = outputs.transform_poses_to_original_space(outputs.cameras.camera_to_worlds, "opencv").numpy()
+    dataparser_poses = np.concatenate(
+        (dataparser_poses, np.array([[[0, 0, 0, 1]]]).repeat(len(dataparser_poses), 0)), 1
+    )
+    dataparser_poses = np.linalg.inv(dataparser_poses)
+    np.testing.assert_allclose(original_poses, dataparser_poses, rtol=0, atol=1e-5)