diff --git a/nerfstudio/data/datamanagers/base_datamanager.py b/nerfstudio/data/datamanagers/base_datamanager.py
index 4fc1822dd..d032c4d48 100644
--- a/nerfstudio/data/datamanagers/base_datamanager.py
+++ b/nerfstudio/data/datamanagers/base_datamanager.py
@@ -38,6 +38,7 @@ from nerfstudio.configs.base_config import InstantiateConfig
 from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
 from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
 from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
+from nerfstudio.data.dataparsers.dycheck_dataparser import DycheckDataParserConfig
 from nerfstudio.data.dataparsers.friends_dataparser import FriendsDataParserConfig
 from nerfstudio.data.dataparsers.instant_ngp_dataparser import (
     InstantNGPDataParserConfig,
@@ -73,6 +74,7 @@ AnnotatedDataParserUnion = tyro.conf.OmitSubcommandPrefixes[  # Omit prefixes of
             "nuscenes-data": NuScenesDataParserConfig(),
             "dnerf-data": DNeRFDataParserConfig(),
             "phototourism-data": PhototourismDataParserConfig(),
+            "dycheck-data": DycheckDataParserConfig(),
         },
         prefix_names=False,  # Omit prefixes in subcommands themselves.
     )
diff --git a/nerfstudio/data/dataparsers/dycheck_dataparser.py b/nerfstudio/data/dataparsers/dycheck_dataparser.py
new file mode 100644
index 000000000..92cca35ff
--- /dev/null
+++ b/nerfstudio/data/dataparsers/dycheck_dataparser.py
@@ -0,0 +1,342 @@
+# Copyright 2022 The Nerfstudio Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Data parser for DyCheck (https://arxiv.org/abs/2210.13445) dataset of `iphone` subset"""
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, List, Tuple, Type
+
+import cv2
+import numpy as np
+import torch
+from rich.progress import Console
+
+from nerfstudio.cameras.cameras import Cameras, CameraType
+from nerfstudio.data.dataparsers.base_dataparser import (
+    DataParser,
+    DataParserConfig,
+    DataparserOutputs,
+)
+from nerfstudio.data.scene_box import SceneBox
+from nerfstudio.utils.colors import get_color
+from nerfstudio.utils.io import load_from_json
+
+CONSOLE = Console(width=120)
+
+
+def downscale(img, scale: int) -> np.ndarray:
+    """Function from DyCheck's repo. Downscale an image.
+
+    Args:
+        img: Input image
+        scale: Factor of the scale
+
+    Returns:
+        New image
+    """
+    if scale == 1:
+        return img
+    height, width = img.shape[:2]
+    if height % scale > 0 or width % scale > 0:
+        raise ValueError(f"Image shape ({height},{width}) must be divisible by the" f" scale ({scale}).")
+    out_height, out_width = height // scale, width // scale
+    resized = cv2.resize(img, (out_width, out_height), cv2.INTER_AREA)
+    return resized
+
+
+def upscale(img, scale: int) -> np.ndarray:
+    """Function from DyCheck's repo. Upscale an image.
+
+    Args:
+        img: Input image
+        scale: Factor of the scale
+
+    Returns:
+        New image
+    """
+    if scale == 1:
+        return img
+    height, width = img.shape[:2]
+    out_height, out_width = height * scale, width * scale
+    resized = cv2.resize(img, (out_width, out_height), cv2.INTER_AREA)
+    return resized
+
+
+def rescale(img, scale_factor: float, interpolation=cv2.INTER_AREA) -> np.ndarray:
+    """Function from DyCheck's repo. Rescale an image.
+
+    Args:
+        img: Input image
+        scale: Factor of the scale
+        interpolation: Interpolation method in opencv
+
+    Returns:
+        New image
+    """
+    scale_factor = float(scale_factor)
+    if scale_factor <= 0.0:
+        raise ValueError("scale_factor must be a non-negative number.")
+    if scale_factor == 1.0:
+        return img
+
+    height, width = img.shape[:2]
+    if scale_factor.is_integer():
+        return upscale(img, int(scale_factor))
+
+    inv_scale = 1.0 / scale_factor
+    if inv_scale.is_integer() and (scale_factor * height).is_integer() and (scale_factor * width).is_integer():
+        return downscale(img, int(inv_scale))
+
+    print(f"Resizing image by non-integer factor {scale_factor}, this may lead to artifacts.")
+    height, width = img.shape[:2]
+    out_height = math.ceil(height * scale_factor)
+    out_height -= out_height % 2
+    out_width = math.ceil(width * scale_factor)
+    out_width -= out_width % 2
+
+    return cv2.resize(img, (out_width, out_height), interpolation)
+
+
+def _load_scene_info(data_dir: Path) -> Tuple[np.ndarray, float, float, float]:
+    """Function from DyCheck's repo. Load scene info from json.
+
+    Args:
+        data_dir: data path
+
+    Returns:
+        A tuple of scene info: center, scale, near, far
+    """
+    scene_dict = load_from_json(data_dir / "scene.json")
+    center = np.array(scene_dict["center"], dtype=np.float32)
+    scale = scene_dict["scale"]
+    near = scene_dict["near"]
+    far = scene_dict["far"]
+    return center, scale, near, far
+
+
+def _load_metadata_info(data_dir: Path) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Function from DyCheck's repo. Load scene metadata from json.
+
+    Args:
+        data_dir: data path
+
+    Returns:
+        A tuple of scene info: frame_names_map, time_ids, camera_ids
+    """
+    dataset_dict = load_from_json(data_dir / "dataset.json")
+    _frame_names = np.array(dataset_dict["ids"])
+
+    metadata_dict = load_from_json(data_dir / "metadata.json")
+    time_ids = np.array([metadata_dict[k]["warp_id"] for k in _frame_names], dtype=np.uint32)
+    camera_ids = np.array([metadata_dict[k]["camera_id"] for k in _frame_names], dtype=np.uint32)
+
+    frame_names_map = np.zeros((time_ids.max() + 1, camera_ids.max() + 1), _frame_names.dtype)
+    for i, (t, c) in enumerate(zip(time_ids, camera_ids)):
+        frame_names_map[t, c] = _frame_names[i]
+
+    return frame_names_map, time_ids, camera_ids
+
+
+def _rescale_depth(depth_raw: np.ndarray, cam: Dict) -> np.ndarray:
+    """Depth rescale function from DyCheck.
+
+    Args:
+        depth: A numpy ndarray of the raw depth
+        cam: Dict of the camera
+
+    Returns:
+        A numpy ndarray of the processed depth
+    """
+    xx, yy = np.meshgrid(np.arange(cam["width"], dtype=np.float32), np.arange(cam["height"], dtype=np.float32))
+    pixels = np.stack([xx, yy], axis=-1)
+    batch_shape = pixels.shape[:-1]
+    pixels = np.reshape(pixels, (-1, 2))
+    y = (pixels[..., 1] - cam["cy"]) / cam["fy"]
+    x = (pixels[..., 0] - cam["cx"]) / cam["fx"]
+    # x = (pixels[..., 0] - self.principal_point_x - y * self.skew) / self.scale_factor_x
+    # assume skew = 0
+    viewdirs = np.stack([x, y, np.ones_like(x)], axis=-1)
+    local_viewdirs = viewdirs / np.linalg.norm(viewdirs, axis=-1, keepdims=True)
+    viewdirs = (cam["camera_to_worlds"][:3, :3] @ local_viewdirs[..., None])[..., 0]
+    viewdirs /= np.linalg.norm(viewdirs, axis=-1, keepdims=True)
+    viewdirs = viewdirs.reshape((*batch_shape, 3))
+    cosa = viewdirs @ (cam["camera_to_worlds"][:, 2])
+    depth = depth_raw / cosa[..., None]
+    return depth
+
+
+@dataclass
+class DycheckDataParserConfig(DataParserConfig):
+    """Dycheck (https://arxiv.org/abs/2210.13445) dataset parser config"""
+
+    _target: Type = field(default_factory=lambda: Dycheck)
+    """target class to instantiate"""
+    data: Path = Path("data/iphone/mochi-high-five")
+    """Directory specifying location of data."""
+    scale_factor: float = 5.0
+    """How much to scale the camera origins by."""
+    alpha_color: str = "white"
+    """alpha color of background"""
+    downscale_factor: int = 1
+    """How much to downscale images."""
+    scene_box_bound: float = 1.5
+    """Boundary of scene box."""
+
+
+@dataclass
+class Dycheck(DataParser):
+    """Dycheck (https://arxiv.org/abs/2210.13445) Dataset `iphone` subset"""
+
+    config: DycheckDataParserConfig
+
+    def __init__(self, config: DycheckDataParserConfig):
+        super().__init__(config=config)
+        self.data: Path = config.data
+        self.scale_factor: float = config.scale_factor
+        self.alpha_color = config.alpha_color
+        # load extra info from "extra.json"
+        extra_path = self.data / "extra.json"
+        extra_dict = load_from_json(extra_path)
+        self._factor = extra_dict["factor"]
+        self._fps = extra_dict["fps"]
+        self._bbox = np.array(extra_dict["bbox"], dtype=np.float32)
+        self._lookat = np.array(extra_dict["lookat"], dtype=np.float32)
+        self._up = np.array(extra_dict["up"], dtype=np.float32)
+        self._center, self._scale, self._near, self._far = _load_scene_info(self.data)
+        self._frame_names_map, self._time_ids, self._camera_ids = _load_metadata_info(self.data)
+
+    def _generate_dataparser_outputs(self, split="train"):
+        if self.alpha_color is not None:
+            alpha_color_tensor = get_color(self.alpha_color)
+        else:
+            alpha_color_tensor = None
+        splits_dir = self.data / "splits"
+
+        # scale the scene to fill the aabb bbox
+        sf = self.config.scene_box_bound / 4 / (self._scale * self._far)
+        # CONSOLE.print(f"scale factor changed from {self.config.scale_factor} to {sf}")
+        self.config.scale_factor = sf
+
+        if not (splits_dir / f"{split}.json").exists():
+            CONSOLE.print(f"split {split} not found, using split train")
+            split = "train"
+        split_dict = load_from_json(splits_dir / f"{split}.json")
+        frame_names = np.array(split_dict["frame_names"])
+        time_ids = np.array(split_dict["time_ids"])
+        if split != "train":
+            CONSOLE.print(f"split {split} is empty, using the 1st training image")
+            split_dict = load_from_json(splits_dir / "train.json")
+            frame_names = np.array(split_dict["frame_names"])[[0]]
+            time_ids = np.array(split_dict["time_ids"])[[0]]
+
+        image_filenames, depth_filenames, cams = self.process_frames(frame_names, time_ids)
+
+        scene_box = SceneBox(
+            aabb=torch.tensor(
+                [[-self.config.scene_box_bound] * 3, [self.config.scene_box_bound] * 3], dtype=torch.float32
+            )
+        )
+        cam_dict = {}
+        for k in cams[0].keys():
+            cam_dict[k] = torch.stack([torch.as_tensor(c[k]) for c in cams], dim=0)
+        cameras = Cameras(camera_type=CameraType.PERSPECTIVE, **cam_dict)
+
+        scale = self._scale * self.config.scale_factor
+        dataparser_outputs = DataparserOutputs(
+            image_filenames=image_filenames,
+            cameras=cameras,
+            alpha_color=alpha_color_tensor,
+            scene_box=scene_box,
+            metadata={
+                "depth_filenames": depth_filenames,
+                "depth_unit_scale_factor": scale,
+                "scale": scale,
+                "near": self._near * scale,
+                "far": self._far * scale,
+            },
+        )
+
+        return dataparser_outputs
+
+    def process_frames(self, frame_names: List[str], time_ids: np.ndarray) -> Tuple[List, List, List]:
+        """Read cameras and filenames from the name list.
+
+        Args:
+            frame_names: list of file names.
+            time_ids: time id of each frame.
+
+        Returns:
+            A list of camera, each entry is a dict of the camera.
+        """
+        image_filenames, depth_filenames = [], []
+        cams = []
+        for idx, frame in enumerate(frame_names):
+            image_filenames.append(self.data / f"rgb/{self.config.downscale_factor}x/{frame}.png")
+            depth_filenames.append(self.data / f"processed_depth/{self.config.downscale_factor}x/{frame}.npy")
+            cam_json = load_from_json(self.data / f"camera/{frame}.json")
+            c2w = torch.as_tensor(cam_json["orientation"]).T
+            position = torch.as_tensor(cam_json["position"])
+            position -= self._center  # some scenes look weird (wheel)
+            position *= self._scale * self.config.scale_factor
+            pose = torch.zeros([3, 4])
+            pose[:3, :3] = c2w
+            pose[:3, 3] = position
+            # from opencv coord to opengl coord (used by nerfstudio)
+            pose[0:3, 1:3] *= -1  # switch cam coord x,y
+            pose = pose[[1, 0, 2], :]  # switch world x,y
+            pose[2, :] *= -1  # invert world z
+            # for aabb bbox usage
+            pose = pose[[1, 2, 0], :]  # switch world xyz to zxy
+            cams.append(
+                {
+                    "camera_to_worlds": pose,
+                    "fx": cam_json["focal_length"] / self.config.downscale_factor,
+                    "fy": cam_json["focal_length"] * cam_json["pixel_aspect_ratio"] / self.config.downscale_factor,
+                    "cx": cam_json["principal_point"][0] / self.config.downscale_factor,
+                    "cy": cam_json["principal_point"][1] / self.config.downscale_factor,
+                    "height": cam_json["image_size"][1] // self.config.downscale_factor,
+                    "width": cam_json["image_size"][0] // self.config.downscale_factor,
+                    "times": torch.as_tensor(time_ids[idx] / self._time_ids.max()).float(),
+                }
+            )
+
+        d = self.config.downscale_factor
+        if not image_filenames[0].exists():
+            CONSOLE.print(f"downscale factor {d}x not exist, converting")
+            ori_h, ori_w = cv2.imread(str(self.data / f"rgb/1x/{frame_names[0]}.png")).shape[:2]
+            (self.data / f"rgb/{d}x").mkdir(exist_ok=True)
+            h, w = ori_h // d, ori_w // d
+            for frame in frame_names:
+                cv2.imwrite(
+                    str(self.data / f"rgb/{d}x/{frame}.png"),
+                    cv2.resize(cv2.imread(str(self.data / f"rgb/1x/{frame}.png")), [h, w]),
+                )
+            CONSOLE.print("finished")
+
+        if not depth_filenames[0].exists():
+            CONSOLE.print(f"processed depth downscale factor {d}x not exist, converting")
+            (self.data / f"processed_depth/{d}x").mkdir(exist_ok=True, parents=True)
+            for idx, frame in enumerate(frame_names):
+                depth = np.load(self.data / f"depth/1x/{frame}.npy")
+                mask = rescale((depth != 0).astype(np.uint8) * 255, 1 / d, cv2.INTER_AREA)
+                depth = rescale(depth, 1 / d, cv2.INTER_AREA)
+                depth[mask != 255] = 0
+                depth = _rescale_depth(depth, cams[idx])
+                np.save(str(self.data / f"processed_depth/{d}x/{frame}.npy"), depth)
+            CONSOLE.print("finished")
+
+        return image_filenames, depth_filenames, cams
diff --git a/nerfstudio/data/utils/data_utils.py b/nerfstudio/data/utils/data_utils.py
index c4b21febd..f04fb368c 100644
--- a/nerfstudio/data/utils/data_utils.py
+++ b/nerfstudio/data/utils/data_utils.py
@@ -64,8 +64,8 @@ def get_depth_image_from_path(
     interpolation: int = cv2.INTER_NEAREST,
 ) -> torch.Tensor:
     """Loads, rescales and resizes depth images.
+    Filepath points to a 16-bit or 32-bit depth image, or a numpy array `*.npy`.
 
-    Assumes filepath points to a 16-bit or 32-bit depth image.
     Args:
         filepath: Path to depth image.
         height: Target depth image height.
@@ -76,7 +76,11 @@ def get_depth_image_from_path(
     Returns:
         Depth image torch tensor with shape [width, height, 1].
     """
-    image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)
-    image = image.astype(np.float64) * scale_factor
-    image = cv2.resize(image, (width, height), interpolation=interpolation)
+    if filepath.suffix == ".npy":
+        image = np.load(filepath) * scale_factor
+        image = cv2.resize(image, (width, height), interpolation=interpolation)
+    else:
+        image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)
+        image = image.astype(np.float64) * scale_factor
+        image = cv2.resize(image, (width, height), interpolation=interpolation)
     return torch.from_numpy(image[:, :, np.newaxis])