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This example demonstrates the ability to read and visualize scenes from the nuScenes dataset, which is a public large-scale dataset specifically designed for autonomous driving. The scenes in this dataset encompass data collected from a comprehensive suite of sensors on autonomous vehicles. These include 6 cameras, 1 LIDAR, 5 RADAR, GPS and IMU sensors. Consequently, the dataset provides information about the vehicle's pose, the images captured, the recorded sensor data and the results of object detection at any given moment.

Logging and visualizing with Rerun

The visualizations in this example were created with the following Rerun code:

Sensor calibration

First, pinhole cameras and sensor poses are initialized to offer a 3D view and camera perspective. This is achieved using the Pinhole and Transform3D archetypes.

rr.log(
    f"world/ego_vehicle/{sensor_name}",
    rr.Transform3D(
        translation=calibrated_sensor["translation"],
        rotation=rr.Quaternion(xyzw=rotation_xyzw),
        from_parent=False,
    ),
    timeless=True,
)

rr.log(
    f"world/ego_vehicle/{sensor_name}",
    rr.Pinhole(
        image_from_camera=calibrated_sensor["camera_intrinsic"],
        width=sample_data["width"],
        height=sample_data["height"],
    ),
    timeless=True,
)

Timelines

All data logged using Rerun in the following sections is initially connected to a specific time. Rerun assigns a timestamp to each piece of logged data, and these timestamps are associated with timelines.

rr.set_time_seconds("timestamp", sample_data["timestamp"] * 1e-6)

Vehicle pose

As the vehicle is moving, its pose needs to be updated. Consequently, the positions of pinhole cameras and sensors must also be adjusted using Transform3D.

rr.log(
    "world/ego_vehicle",
    rr.Transform3D(
        translation=ego_pose["translation"],
        rotation=rr.Quaternion(xyzw=rotation_xyzw),
        from_parent=False,
    ),
)

LiDAR data

LiDAR data is logged as Points3D archetype.

rr.log(f"world/ego_vehicle/{sensor_name}", rr.Points3D(points, colors=point_colors))

Camera data

Camera data is logged as encoded images using ImageEncoded.

rr.log(f"world/ego_vehicle/{sensor_name}", rr.ImageEncoded(path=data_file_path))

Radar data

Radar data is logged similar to LiDAR data, as Points3D.

rr.log(f"world/ego_vehicle/{sensor_name}", rr.Points3D(points, colors=point_colors))

Annotations

Annotations are logged as Boxes3D, containing details such as object positions, sizes, and rotation.

rr.log("world/anns", rr.Boxes3D(sizes=sizes, centers=centers, rotations=rotations, class_ids=class_ids))

Setting up the default blueprint

The default blueprint for this example is created by the following code:

sensor_space_views = [
    rrb.Spatial2DView(
        name=sensor_name,
        origin=f"world/ego_vehicle/{sensor_name}",
    )
    for sensor_name in nuscene_sensor_names(nusc, args.scene_name)
]
blueprint = rrb.Vertical(
    rrb.Spatial3DView(name="3D", origin="world"),
    rrb.Grid(*sensor_space_views),
    row_shares=[3, 2],
)

We programmatically create one view per sensor and arrange them in a grid layout, which is convenient when the number of views can significantly vary from dataset to dataset. This code also showcases the row_shares argument for vertical containers: it can be used to assign a relative size to each of the container's children. A similar column_shares argument exists for horizontal containers, while grid containers accept both.

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import rerun as rr  # pip install rerun-sdk
rr.init("rerun_example_app")

rr.connect()  # Connect to a remote viewer
# rr.spawn()  # Spawn a child process with a viewer and connect
# rr.save("recording.rrd")  # Stream all logs to disk

# Associate subsequent data with 42 on the “frame” timeline
rr.set_time_sequence("frame", 42))

# Log colored 3D points to the entity at `path/to/points`
rr.log("path/to/points", rr.Points3D(positions, colors=colors