Waymo Open Dataset - Perception

The Waymo Open Dataset (WOD) is a collective term for publicly available datasets from Waymo. The Perception Dataset, abbreviated as WOD-P, is a high-quality dataset targeted for perceptions tasks, such as With 1150 logs each spanning 20 seconds, the dataset includes about 6.4 hours

Overview

Available Modalities

Name

Available

Description

Ego Vehicle

State of the ego vehicle, including poses, and vehicle parameters, see EgoStateSE3.

Map

(✓)

The HD-Maps are in 3D, but may have artifacts due to polyline to polygon conversion (see below). For more information, see MapAPI.

Bounding Boxes

The bounding boxes are available with the WOPDBoxDetectionLabel. For more information, BoxDetectionWrapper.

Traffic Lights

X

n/a

Pinhole Cameras

Includes 5 cameras, see PinholeCamera:

Fisheye Cameras

X

n/a

LiDARs

Includes 5 LiDARs, see LiDAR:

Dataset Specific
class py123d.conversion.registry.WOPDBoxDetectionLabel[source]

Semantic labels for Waymo Open Dataset bounding box detections. [1] https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/labeling_specifications.md [2] https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/label.proto#L63-L69

TYPE_UNKNOWN = 0
TYPE_VEHICLE = 1
TYPE_PEDESTRIAN = 2
TYPE_SIGN = 3
TYPE_CYCLIST = 4
to_default()[source]

Inherited, see superclass.

Return type:

DefaultBoxDetectionLabel

class py123d.conversion.registry.WOPDLiDARIndex[source]

Waymo Open Perception Dataset (WOPD) LiDAR Indexing Scheme, with polar features.

RANGE = 0
INTENSITY = 1
ELONGATION = 2
X = 3
Y = 4
Z = 5

Download

To download the Waymo Open Dataset for Perception, please visit the official website and follow the instructions provided there. You will need to register and download the Perception Dataset V1.4.3. (We currently do not support V2.0.1 due to the missing maps.) The expected directory structure after downloading and extracting the dataset is as follows:

$WODP_DATA_ROOT
  ├── testing/
  |   ├── segment-10084636266401282188_1120_000_1140_000_with_camera_labels.tfrecord
  |   ├── ...
  |   └── segment-9806821842001738961_4460_000_4480_000_with_camera_labels.tfrecord
  ├── training/
  |   ├── segment-10017090168044687777_6380_000_6400_000_with_camera_labels.tfrecord
  |   ├── ...
  |   └── segment-9985243312780923024_3049_720_3069_720_with_camera_labels.tfrecord
  └── validation/
      ├── segment-10203656353524179475_7625_000_7645_000_with_camera_labels.tfrecord
      ├── ...
      └── segment-967082162553397800_5102_900_5122_900_with_camera_labels.tfrecord

You can add the dataset root directory to the environment variable WODP_DATA_ROOT for easier access.

export WODP_DATA_ROOT=/path/to/wodp_dataset_root

Optionally, you can adjust the py123d/script/config/common/default_dataset_paths.yaml accordingly.

Installation

The Waymo Open Dataset requires additional dependencies that are included as optional dependencies in py123d. You can install them via:

pip install py123d[waymo]
pip install -e .[waymo]

These dependencies are notoriously difficult to install due to compatibility issues. We recommend using a dedicated conda environment for this purpose. Using uv can significantly speed up the installation. Here is an example of how to set it up:

conda create -n py123d_waymo python=3.10
conda activate py123d_waymo
uv pip install -e .[waymo]
# If something goes wrong: conda deactivate; conda remove -n py123d_waymo --all

You only need the Waymo Open Dataset specific dependencies if you convert the dataset or read from the raw TFRecord files. After conversion, you may use any other py123d installation.

Dataset Specific Issues

  • Map: The HD-Map in Waymo has bugs …

Citation

If you use this dataset in your research, please cite:

@inproceedings{Sun2020CVPR,
  title={Scalability in perception for autonomous driving: Waymo open dataset},
  author={Sun, Pei and Kretzschmar, Henrik and Dotiwalla, Xerxes and Chouard, Aurelien and Patnaik, Vijaysai and Tsui, Paul and Guo, James and Zhou, Yin and Chai, Yuning and Caine, Benjamin and others},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={2446--2454},
  year={2020}
}