Waymo Open Dataset (WOD) 数据集详解（比官网详细版）

Waymo Open Dataset

This is a compensate document for how to access different features of the dataset.

What the official doc gives us

• How to install all the utils created by themselves.
• Some text description of the dataset and the data inside.
• We use “the doc” to refer to the official documentation.

What’s new

• How to really access the dataset. Using code, not human language description.
• This is some kind of explanation of the tutorial provided in the doc.
• Maybe some troubleshooting works. Just maybe.

Data format and how to load

This dataset is stored in .tfrecord files. We first create a TFRecordDataset, whose first argument is a list of all paths of the .tfrecord files.

train_set = tf.data.TFRecordDataset(train_files, compression_type='')
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Then we can iterate over the train_set. Each sample in the dataset is a frame carrying all kinds of information.

from waymo_open_dataset import dataset_pb2 as open_dataset
for i, data in enumerate(train_set):
    frame = open_dataset.Frame()
    frame.ParseFromString(bytearray(data.numpy()))
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Here, waymo_open_dataset is the pre-configured package, which is already instructed in the doc. In the for loop, the frame loads all the information from data. We now focus on this single frame and see what’s inside.

Tree structure of the dataset classes

• If a node has ⇒ specifying a list of XXX, then the sub-branch (if any) is for each element in the list.
• Capitalized nodes are class names in the source code of the dataset. You can simply convert them to lowercase when writing codes.
  • For 1xample: frame.context.stats.location
  • For 2xample: frame.camera_labels[0].labels[0].box.length

open_dataset
|-- LaserName
|   |-- UNKNOWN
|   |-- TOP
|   |-- FRONT
|   |-- SIDE_LEFT
|   |-- SIDE_RIGHT
|   `-- REAR
|-- CameraName
|   |-- UNKNOWN
|   |-- FRONT
|   |-- FRONT_LEFT
|   |-- FRONT_RIGHT
|   |-- SIDE_LEFT
|   `-- SIDE_RIGHT
|-- RollingShutterReadOutDirection
|   |-- UNKNOWN
|   |-- TOP_TO_BOTTOM
|   |-- LEFT_TO_RIGHT
|   |-- BOTTOM_TO_TOP
|   |-- RIGHT_TO_LEFT
|   `-- GLOBAL_SHUTTER
|-- Frame
|   |-- images ⇒ list of CameraImage
|   |   |-- name (CameraName)
|   |   |-- image
|   |   |-- pose
|   |   |-- velocity (v_x, v_y, v_z, w_x, w_y, w_z)
|   |   |-- pose_timestamp
|   |   |-- shutter
|   |   |-- camera_trigger_time
|   |   `-- camera_readout_done_time
|   |-- Context
|   |   |-- name
|   |   |-- camera_calibrations ⇒ list of CameraCalibration
|   |   |   |-- name
|   |   |   |-- intrinsic
|   |   |   |-- extrinsic
|   |   |   |-- width
|   |   |   |-- height
|   |   |   `-- rolling_shutter_direction (RollingShutterReadOutDirection)
|   |   |-- laser_calibrations ⇒ list of LaserCalibration
|   |   |   |-- name
|   |   |   |-- beam_inclinations
|   |   |   |-- beam_inclination_min
|   |   |   |-- beam_inclination_max
|   |   |   `-- extrinsic
|   |   `-- Stats
|   |       |-- laser_object_counts
|   |       |-- camera_object_counts
|   |       |-- time_of_day
|   |       |-- location
|   |       `-- weather
|   |-- timestamp_micros
|   |-- pose
|   |-- lasers ⇒ list of Laser
|   |   |-- name (LaserName)
|   |   |-- ri_return1 (RangeImage class)
|   |   |   |-- range_image_compressed
|   |   |   |-- camera_projection_compressed
|   |   |   |-- range_image_pose_compressed
|   |   |   `-- range_image
|   |   `-- ri_return2 (same as ri_return1)
|   |-- laser_labels ⇒ list of Label
|   |-- projected_lidar_labels (same as camera_labels)
|   |-- camera_labels ⇒ list of CameraLabels
|   |   |-- name (CameraName)
|   |   `-- labels ⇒ list of Label
|   `-- no_label_zones (Refer to the doc)
`-- Label
    |-- Box
    |   |-- center_x
    |   |-- center_y
    |   |-- center_z
    |   |-- length
    |   |-- width
    |   |-- height
    |   `-- heading
    |-- Metadata
    |   |-- speed_x
    |   |-- speed_y
    |   |-- accel_x
    |   `-- accel_y
    |-- type
    |-- id
    |-- detection_difficulty_level
    `-- tracking_difficulty_level

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We will only explore the frame in detail. Others please refer to this tree. Also for the 6 attributes in velocity, I still dunno what they are.

Attributes of frame

LiDAR data

Lidar data are given by frame.lasers. It’s an iterable of raw laser data, indicating different lidar positions: top, front, side left, side right, rear. Each element has an attribute .name telling the name of its lidar. The lidar names are stored as constants in the open_dataset package (similar to macro):

open_dataset.LaserName.UNKNOWN    = 0
open_dataset.LaserName.TOP        = 1
open_dataset.LaserName.FRONT      = 2
open_dataset.LaserName.SIDE_LEFT  = 3
open_dataset.LaserName.SIDE_RIGHT = 4
open_dataset.LaserName.REAR       = 5
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We may want to use point cloud data. In the tutorial given by the doc, they provide a function parse_range_image_and_camera_projection:

(range_images, camera_projections, range_image_top_pose) = 
parse_range_image_and_camera_projection(frame)
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The range_images here has two dimensions, e.g., range_images[2][0].

• The first dimension [2] indicates which lidar

  2 here is front lidar. Refer to LaserName

• The second dimension [0] is for the strongest two intensity returns

  0 is for the strongest, 1 is for the second strongest.
  There might be some data that has only one return. I haven’t tested yet, so I put “might be”.

• range_images[2][0] is a tuple with length of 4, consistent with the “4 channels” listed in the Lidar Data section in the doc.

  range_images[2][0].shape returns (..., ..., 4)

After that, there is convert_range_image_to_point_cloud:

points, cp_points = convert_range_image_to_point_cloud(frame,
                                                       range_images,
                                                       camera_projections,
                                                       range_image_top_pose)
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• It converts range_images into point cloud, points.
• It’s a {[N, 3]} list of 3D lidar points of length 5 (number of lidars).

  len(points) returns 5.
  points[2].shape returns (..., 3).

• The returned 3D lidar data is in cartesian coordinate system.

  Each element in points[2] is (x,y,z) coordinate.

Camera images

Camera images are given by frame.images. It’s an iterable of images, indicating different camera positions: front, front left, front right, side left, side right. Each element has an attribute .name telling the name of its camera. The camera names are stored as constants in the open_dataset package (similar to macro):

open_dataset.CameraName.UNKNOWN     = 0
open_dataset.CameraName.FRONT       = 1
open_dataset.CameraName.FRONT_LEFT  = 2
open_dataset.CameraName.FRONT_RIGHT = 3
open_dataset.CameraName.SIDE_LEFT   = 4
open_dataset.CameraName.SIDE_RIGHT  = 5
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Each element also has an attribute .image that stores the real image. The camera images are of JPEG format. To save them to local storage:

from PIL import Image
import io
image = Image.open(io.BytesIO(frame.images[0].image))
image.save(filename, 'JPEG')
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Camera projection of lidar data

TODO: The image together with depth information provided by point cloud projection. There’s also projected labels. For current stage, I don’t think we are using this part of information.

Bounding box labels

2D labels

front_label = None
for lbl in frame.camera_labels:
    if lbl.name == open_dataset.CameraName.FRONT:
        front_label = lbl
        break

for label in front_label.labels:
    if label.type == 1:
        pass
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• 2D bbox for camera images
• Only first 3 batches of the training set and the first batch of validation set have 2D labels.
• Stored in frame.camera_labels, which has 5 elements indicating 5 different cameras.
  • Each element lbl has attribute .name, same as CameraName, corresponding to one specific camera.
  • Each element contains all bbox information in the image taken by that specific camera.
• In the example, we get the front_label for the image taken by the front camera. In front_label.labels, it stores all bbox labels in that single image.
  • Each label has an attribute .type:

    For the constants of different types please refer to their github repo.
    Here 1 means vehicle.

  • Bbox is given by box = label.box
• In a nutshell, the access chain for bbox is box = frame.camera_labels[0].labels[0].box.
• Now we look into the box:
  • box.width is the top-bottom distance of the image.
  • box.length is the left-right distance of the image.
  • box.center_x x-coordinate of the image is along the length direction.
  • box.center_y y-coordinate of the image is along the width direction.

3D labels

for label in frame.laser_labels:
    if label.type == 1:
        pass
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• This is different from camera labels. It does not distinguish which lidar it’s using. frame.laser_labels simply stores all bboxes in all directions in that frame.

  The doc provides no information on how these labels are related to different lidars.

• The other attributes of label are pretty similar. Except that the box is 3D. Here are the correspondence:

  • box.length <=> box.center_x along the “front/forward” direction.
  • box.width <=> box.center_y along the “left” direction.
  • box.height <=> box.center_z along the “upward/pointing-to-the-sky” direction.
  • Please refer to the doc about Vehicle frame in the Coordinate systems section.

Misc

This subsubsection lists all other features that might potentially be useful.

• For waymo.open_dataset.Label class, aka the label variable as whose instance in previous section, they have metadata for speed and acceleration.
  • label.metadata.speed_x
  • label.metadata.speed_y
  • label.metadata.accel_x
  • label.metadata.accel_y
  • The unit of the data still unknown.
• However, only 3D labels have such metadata. (Note that these are for bboxes, not for the ego car itself). The data for the car itself are stored in the image.velocity (refer to the tree structure), but what does w_x stand for? Angular velocity?

Troubleshooting

• tf.enable_eager_execution() is required for tf1.x to load the dataset. tf2.x has eager execution by default.
• Built dataset using pip3 install in the tutorial, but still cannot import dataset_pb2:
  • Solution: use protoc to manually generate python files like in this issue
• Using code based on Kitti dataset? See this repo for a convertion tool. It fixed the difference in the coordination systems between Waymo and Kitti dataset, so you can visualize the dataset with kitti_object_vis with no trouble.
• How to read gt.bin file: this issue.

Differentiable LiDAR-to-depth

• Get gradients of LiDAR point coordinates:
  • In the tutorial, they already showed us how to project points onto the image with points_all and cp_points_all.
  • cp_points_all stores the image pixel location, and the norm of points_all is the depth value of the corresponding pixel.
• Modifying the original point cloud:
  • After modifying the coordinates, we need to re-generate cp_points_all.
  • They provided a third party tool for this, please refer to this issue.
  • Note that the generation of pixel location (cp_points_all) doesn’t have to be differentiable, so we can just use the third party tool.

---

Reference:
@misc{waymo_open_dataset,
title = {Waymo Open Dataset: An autonomous driving dataset},
website = {\url{https://www.waymo.com/open}},
year = {2019}
}