diff --git a/projects/nerf/nerf/dataset.py b/projects/nerf/nerf/dataset.py
new file mode 100644
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+++ b/projects/nerf/nerf/dataset.py
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+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+import os
+from typing import Tuple, Optional, List
+
+import numpy as np
+import requests
+import torch
+from PIL import Image
+from pytorch3d.renderer import PerspectiveCameras
+from torch.utils.data import Dataset
+
+DEFAULT_DATA_ROOT = os.path.join(
+    os.path.dirname(os.path.realpath(__file__)), "..", "data"
+)
+
+DEFAULT_URL_ROOT = "https://dl.fbaipublicfiles.com/pytorch3d_nerf_data"
+
+ALL_DATASETS = ("lego", "fern", "pt3logo")
+
+
+def trivial_collate(batch):
+    """
+    A trivial collate function that merely returns the uncollated batch.
+    """
+    return batch
+
+
+class ListDataset(Dataset):
+    """
+    A simple dataset made of a list of entries.
+    """
+
+    def __init__(self, entries: List):
+        """
+        Args:
+            entries: The list of dataset entries.
+        """
+        self._entries = entries
+
+    def __len__(
+        self,
+    ):
+        return len(self._entries)
+
+    def __getitem__(self, index):
+        return self._entries[index]
+
+
+def get_nerf_datasets(
+    dataset_name: str,  # 'lego | fern'
+    image_size: Tuple[int, int],
+    data_root: str = DEFAULT_DATA_ROOT,
+    autodownload: bool = True,
+) -> Tuple[Dataset, Dataset, Dataset]:
+    """
+    Obtains the training and validation dataset object for a dataset specified
+    with the `dataset_name` argument.
+
+    Args:
+        dataset_name: The name of the dataset to load.
+        image_size: A tuple (height, width) denoting the sizes of the loaded dataset images.
+        data_root: The root folder at which the data is stored.
+        autodownload: Auto-download the dataset files in case they are missing.
+
+    Returns:
+        train_dataset: The training dataset object.
+        val_dataset: The validation dataset object.
+        test_dataset: The testing dataset object.
+    """
+
+    if dataset_name not in ALL_DATASETS:
+        raise ValueError(f"'{dataset_name}'' does not refer to a known dataset.")
+
+    print(f"Loading dataset {dataset_name}, image size={str(image_size)} ...")
+
+    cameras_path = os.path.join(data_root, dataset_name + ".pth")
+    image_path = cameras_path.replace(".pth", ".png")
+
+    if autodownload and any(not os.path.isfile(p) for p in (cameras_path, image_path)):
+        # Automatically download the data files if missing.
+        download_data((dataset_name,), data_root=data_root)
+
+    train_data = torch.load(cameras_path)
+    n_cameras = train_data["cameras"]["R"].shape[0]
+
+    _image_max_image_pixels = Image.MAX_IMAGE_PIXELS
+    Image.MAX_IMAGE_PIXELS = None  # The dataset image is very large ...
+    images = torch.FloatTensor(np.array(Image.open(image_path))) / 255.0
+    images = torch.stack(torch.chunk(images, n_cameras, dim=0))[..., :3]
+    Image.MAX_IMAGE_PIXELS = _image_max_image_pixels
+
+    scale_factors = [s_new / s for s, s_new in zip(images.shape[1:3], image_size)]
+    if abs(scale_factors[0] - scale_factors[1]) > 1e-3:
+        raise ValueError(
+            "Non-isotropic scaling is not allowed. Consider changing the 'image_size' argument."
+        )
+    scale_factor = sum(scale_factors) * 0.5
+
+    if scale_factor != 1.0:
+        print(f"Rescaling dataset (factor={scale_factor})")
+        images = torch.nn.functional.interpolate(
+            images.permute(0, 3, 1, 2),
+            size=tuple(image_size),
+            mode="bilinear",
+        ).permute(0, 2, 3, 1)
+
+    cameras = [
+        PerspectiveCameras(
+            **{k: v[cami][None] for k, v in train_data["cameras"].items()}
+        ).to("cpu")
+        for cami in range(n_cameras)
+    ]
+
+    train_idx, val_idx, test_idx = train_data["split"]
+
+    train_dataset, val_dataset, test_dataset = [
+        ListDataset(
+            [
+                {"image": images[i], "camera": cameras[i], "camera_idx": int(i)}
+                for i in idx
+            ]
+        )
+        for idx in [train_idx, val_idx, test_idx]
+    ]
+
+    return train_dataset, val_dataset, test_dataset
+
+
+def download_data(
+    dataset_names: Optional[List[str]] = None,
+    data_root: str = DEFAULT_DATA_ROOT,
+    url_root: str = DEFAULT_URL_ROOT,
+):
+    """
+    Downloads the relevant dataset files.
+
+    Args:
+        dataset_names: A list of the names of datasets to download. If `None`,
+            downloads all available datasets.
+    """
+
+    if dataset_names is None:
+        dataset_names = ALL_DATASETS
+
+    os.makedirs(data_root, exist_ok=True)
+
+    for dataset_name in dataset_names:
+        cameras_file = dataset_name + ".pth"
+        images_file = cameras_file.replace(".pth", ".png")
+        license_file = cameras_file.replace(".pth", "_license.txt")
+
+        for fl in (cameras_file, images_file, license_file):
+            local_fl = os.path.join(data_root, fl)
+            remote_fl = os.path.join(url_root, fl)
+
+            print(f"Downloading dataset {dataset_name} from {remote_fl} to {local_fl}.")
+
+            r = requests.get(remote_fl)
+            with open(local_fl, "wb") as f:
+                f.write(r.content)