diff --git a/pcdet/datasets/once/once_toolkits.py b/pcdet/datasets/once/once_toolkits.py
new file mode 100644
index 0000000..ee5666f
--- /dev/null
+++ b/pcdet/datasets/once/once_toolkits.py
@@ -0,0 +1,125 @@
+import json
+import os.path as osp
+from collections import defaultdict
+import cv2
+import numpy as np
+
+class Octopus(object):
+    """
+    dataset structure:
+    - data_root
+        - train_split.txt
+        - val_split.txt
+        - test_split.txt
+        -
+    """
+    camera_names = ['cam01', 'cam03', 'cam05', 'cam06', 'cam07', 'cam08', 'cam09']
+    camera_tags = ['top', 'top2', 'left_back', 'left_front', 'right_front', 'right_back', 'back']
+
+    def __init__(self, dataset_root):
+        self.dataset_root = dataset_root
+        self.data_root = osp.join(self.dataset_root, 'data')
+        self._collect_basic_infos()
+
+    @property
+    def train_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'train_set.txt')):
+            train_split_list = None
+        else:
+            train_split_list = set(map(lambda x: x.strip(),
+                                       open(osp.join(self.data_root, 'train_set.txt')).readlines()))
+        return train_split_list
+
+    @property
+    def val_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'val_set.txt')):
+            val_split_list = None
+        else:
+            val_split_list = set(map(lambda x: x.strip(),
+                                     open(osp.join(self.data_root, 'val_set.txt')).readlines()))
+        return val_split_list
+
+    @property
+    def test_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'test_set.txt')):
+            test_split_list = None
+        else:
+            test_split_list = set(map(lambda x: x.strip(),
+                                       open(osp.join(self.data_root, 'test_set.txt')).readlines()))
+        return test_split_list
+
+    @property
+    def raw_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'raw_set.txt')):
+            raw_split_list = None
+        else:
+            raw_split_list = set(map(lambda x: x.strip(),
+                                       open(osp.join(self.data_root, 'raw_set.txt')).readlines()))
+        return raw_split_list
+
+    def _find_split_name(self, seq_id):
+        if seq_id in self.raw_split_list:
+            return 'raw'
+        if seq_id in self.train_split_list:
+            return 'train'
+        if seq_id in self.test_split_list:
+            return 'test'
+        if seq_id in self.val_split_list:
+            return 'val'
+        print("sequence id {} corresponding to no split".format(seq_id))
+        raise NotImplementedError
+
+    def _collect_basic_infos(self):
+        self.train_info = defaultdict(dict)
+        if self.train_split_list is not None:
+            for train_seq in self.train_split_list:
+                anno_file_path = osp.join(self.data_root, train_seq, '{}.json'.format(train_seq))
+                if not osp.isfile(anno_file_path):
+                    print("no annotation file for sequence {}".format(train_seq))
+                    raise FileNotFoundError
+                anno_file = json.load(open(anno_file_path, 'r'))
+                for frame_anno in anno_file['frames']:
+                    self.train_info[train_seq][frame_anno['frame_id']] = {
+                        'pose': frame_anno['pose'],
+                        'calib': anno_file['calib'],
+                    }
+
+    def get_frame_anno(self, seq_id, frame_id):
+        split_name = self._find_split_name(seq_id)
+        frame_info = getattr(self, '{}_info'.format(split_name))[seq_id][frame_id]
+        if 'anno' in frame_info:
+            return frame_info['anno']
+        return None
+
+    def load_point_cloud(self, seq_id, frame_id):
+        bin_path = osp.join(self.data_root, seq_id, 'lidar_roof', '{}.bin'.format(frame_id))
+        points = np.fromfile(bin_path, dtype=np.float32).reshape(-1, 4)
+        return points
+
+    def load_image(self, seq_id, frame_id, cam_name):
+        cam_path = osp.join(self.data_root, seq_id, cam_name, '{}.jpg'.format(frame_id))
+        img_buf = cv2.cvtColor(cv2.imread(cam_path), cv2.COLOR_BGR2RGB)
+        return img_buf
+
+    def project_lidar_to_image(self, seq_id, frame_id):
+        points = self.load_point_cloud(seq_id, frame_id)
+
+        split_name = self._find_split_name(seq_id)
+        frame_info = getattr(self, '{}_info'.format(split_name))[seq_id][frame_id]
+        points_img_dict = dict()
+        for cam_name in self.__class__.camera_names:
+            calib_info = frame_info['calib'][cam_name]
+            cam_2_velo = calib_info['cam_to_velo']
+            cam_intri = calib_info['cam_intrinsic']
+            point_xyz = points[:, :3]
+            points_homo = np.hstack(
+                [point_xyz, np.ones(point_xyz.shape[0], dtype=np.float32).reshape((-1, 1))])
+            points_lidar = np.dot(points_homo, np.linalg.inv(cam_2_velo).T)
+            mask = points_lidar[:, 2] > 0
+            points_lidar = points_lidar[mask]
+            points_img = np.dot(points_lidar, cam_intri.T)
+            points_img_dict[cam_name] = points_img
+        return points_img_dict
+
+    def undistort_image(self, seq_id, frame_id):
+        pass
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