Sample formats

A sample is a data point you want to label. Samples come in different types, like an image, a 3D point cloud, or a video sequence. When uploading (client.add_sample()) or downloading (client.get_sample()) a sample using the Python SDK, the format of the attributes field depends on the type of sample. The different formats are described here.

The section Import data shows how you can obtain URLs for your assets.

Image

Supported image formats: jpeg, png, bmp.

{
    "image": {
        "url": "https://example.com/image.jpg"
    }
}

If the image file is on your local computer, you should first upload it to our asset storage service (using upload_asset()) or to another cloud storage service.

Image sequence

Supported image formats: jpeg, png, bmp.

{ 
  "frames": [
    {
      "image": {
        "url": "https://example.com/frame_00001.jpg"
      },
      "name": "frame_00001" // optional
    },
    {
      "image": {
        "url": "https://example.com/frame_00002.jpg"
      },
      "name": "frame_00002"
    },
    {
      "image": {
        "url": "https://example.com/frame_00003.jpg"
      },
      "name": "frame_00003"
    }
  ]
}

3D point cloud

On Segments.ai, the up direction is defined along the z-axis, i.e. the vector (0, 0, 1) points up. If you upload point clouds with a different up direction, you might have trouble navigating the point cloud.

{
    "pcd": {
        "url": "https://example.com/pointcloud.pcd",
        "type": "pcd"
    },
    "images": [
        { ... },
        { ... },
        { ... }
    ], // optional
    "name": "frame_00001", // optional
    "timestamp": "00001", // optional
    "ego_pose": {
        "position": {
            "x": -2.7161461413869947,
            "y": 116.25822288149078,
            "z": 1.8348751887989483
        },
        "heading": {
            "qx": -0.02111296123795955,
            "qy": -0.006495469416730261,
            "qz": -0.008024565904865688,
            "qw": 0.9997181192298087
        }
    },
    "default_z": -1, // optional, 0 by default
    "bounds": { // optional
        "min_z": -1,
        "max_z": 3
    }
}
NameTypeDescription

pcd

Point cloud data

Required. Point cloud data.

images

array of camera images

Reference camera images.

name

string

Name of the sample.

timestamp

int, float, or string

Timestamp of the sample. Should be in nanoseconds for accurate velocity/acceleration calculations. Will also be used for interpolation unless disabled in dataset settings.

ego_pose

Ego pose

Pose of the sensor that captured the point cloud data.

default_z

float

Default z-value of the ground plane. 0 by default. Only valid in the point cloud cuboid editor. New cuboids will be drawn on top of the ground plane, i.e. the default z-position of a new cuboid is 0.5 (since the default height of a new cuboid is 1).

bounds

dict

Point cloud bounds: a dict with values min_z and max_z (float). These values are used as the default min/max values for height coloring when provided.

Point cloud data

See 3D point cloud formats for the supported file formats.

{
    "url": "https://example.com/pointcloud.bin",
    "type": "kitti"
}
NameTypeDescription

url

string

Required. URL of the point cloud data.

type

string: "pcd" | "binary-xyzi" | "kitti" | "binary-xyzir" | "nuscenes" | "ply"

If the point cloud file is on your local computer, you should first upload it to our asset storage service (using upload_asset()) or to another cloud storage service.

Camera image

A calibrated or uncalibrated reference image corresponding to a point cloud. The reference images can be opened in a new tab from within the labeling interface. You can determine the layout of the images by setting the row and col attributes on each image. If you also supply the calibration parameters (and distortion parameters if necessary), the main point cloud view can be set to the image to obtain a fused view.

{
    "name": "Camera example 1", // optional
    "url": "https://example.com/image.jpg",
    "row": 0,
    "col": 0,
    "intrinsics": { // optional
        "intrinsic_matrix": [
            [1266.417203046554, 0, 816.2670197447984],
            [0, 1266.417203046554, 491.50706579294757],
            [0, 0, 1]
        ]
    },
    "extrinsics": { // optional
        "translation": {
            "x": -0.012463384576629082,
            "y": 0.76486688894964,
            "z": -0.3109103442096661
        },
        "rotation": {
            "qx": 0.713640516187247,
            "qy": -0.001134052598226082,
            "qz": 0.0036449450274057696,
            "qw": 0.7005017073187271
        }
    },
    "distortion": { // optional
        "model": "fisheye",
        "coefficients": {
            "k1": -0.0539124,
            "k2": -0.0101993,
            "k3": -0.00202017,
            "k4": 0.00120938
        }
    },
    "camera_convention": "OpenCV", // optional
    "rotation": 1.5708 // optional
}
NameTypeDescription

name

string

Name of the camera image.

url

string

Required. URL of the camera image.

row

int

Required. Row of this image in the images viewer.

col

int

Required. Column of this image in the images viewer.

intrinsics

Camera intrinsics

Intrinsic parameters of the camera.

extrinsics

Camera extrinsics

Extrinsic parameters of the camera relative to the ego pose.

distortion

Distortion

Distortion parameters of the camera.

camera_convention

string: "OpenGL" | "OpenCV"

Convention of the camera coordinates. We use the OpenGL/Blender coordinate convention for cameras. +X is right, +Y is up, and +Z is pointing back and away from the camera. -Z is the look-at direction. Other codebases may use the OpenCV convention, where the Y and Z axes are flipped but the +X axis remains the same. See diagram 1.

rotation

float

If the image file is on your local computer, you should first upload it to our asset storage service (using upload_asset()) or to another cloud storage service.

Camera intrinsics

{
    "intrinsic_matrix": [
        [1266.417203046554, 0, 816.2670197447984],
        [0, 1266.417203046554, 491.50706579294757],
        [0, 0, 1]
    ]
}
NameTypeDescription

intrinsic_matrix

Camera extrinsics

{
    "translation": {
        "x": -0.012463384576629082,
        "y": 0.76486688894964,
        "z": -0.3109103442096661
    },
    "rotation": {
        "qx": 0.713640516187247,
        "qy": -0.001134052598226082,
        "qz": 0.0036449450274057696,
        "qw": 0.7005017073187271
    }
}
NameTypeDescription

translation

object: { "x": float, "y": float, "z": float }

Required. Translation of the camera in lidar coordinates, i.e., relative to the ego pose.

rotation

object: { "qx": float, "qy": float, "qz": float,

"qw": float }

Required. Rotation of the camera in lidar coordinates, i.e., relative to the ego pose (or equivalently: a transformation from camera frame to ego frame). Defined as a rotation quaternion. By default, we use the OpenGL/Blender coordinate convention for cameras. +X is right, +Y is up, and +Z is pointing back and away from the camera. -Z is the look-at direction. Other codebases may use the OpenCV convention, where the Y and Z axes are flipped but the +X axis remains the same. See diagram 1. You can specify the camera convention in Camera image.

Distortion

// Fisheye
{ 
    "model": "fisheye",
    "coefficients": {
        "k1": -0.0539124,
        "k2": -0.0101993,
        "k3": -0.00202017,
        "k4": 0.00120938
}
// Brown-Conrady
{ 
    "model": "brown-conrady",
    "coefficients": {
        "k1": -0.2916058942,
        "k2": 0.0763231072,
        "k3": 0.0,
        "p1": 0.0014829263,
        "p2": -0.0019540316
    }
}
NameTypeDescription

model

string: "fisheye" | "brown-conrady"

Required. Type of the distortion model: fisheye or Brown-Conrady.

coefficients

Fisheye: object: {

"k1": float,

"k2": float,

"k3": float,

"k4": float,

}

Brown-Conrady: object: {

"k1": float,

"k2": float,

"k3": float,

"p1": float,

"p2": float

}

Ego pose

The pose of the sensor used to capture the 3D point cloud data. This can be helpful if you want to obtain cuboids in world coordinates, or when your sensor is moving. In the latter situation, supplying an ego pose with each frame will ensure that static objects do not move when switching between frames.

{
    "position": {
        "x": -2.7161461413869947,
        "y": 116.25822288149078,
        "z": 1.8348751887989483
    },
    "heading": {
        "qx": -0.02111296123795955,
        "qy": -0.006495469416730261,
        "qz": -0.008024565904865688,
        "qw": 0.9997181192298087
    }
},
NameTypeDescription

position

object: { "x": float, "y": float, "z": float }

Required. XYZ position of the sensor in world coordinates.

heading

object: { "qx": float, "qy": float, "qz": float,

"qw": float }

Required. Orientation of the sensor. Defined as a rotation quaternion.

Segments.ai uses 32-bit floats for the point positions. Keep in mind that 32-bit floats have limited precision. In fact, only 24 bits can be used to represent the number itself (the significand, excluding the sign bit), or about 7.22 decimal digits. If you want to keep two decimal places, this only leaves 5.22 decimal digits, so the numbers shouldn't be larger than 10^5.22 = 165958.

To avoid rounding problems, it is best practice to subtract the ego position of the first frame from all other ego positions. This way, the first ego position is set to (0, 0, 0) and the subsequent ego positions are relative to (0, 0, 0) . In your export script, you can add the ego position of the first frame back to the object positions.

3D point cloud sequence

{ 
  "frames": [
    { ... },
    { ... },
    { ... }
  ]
}
NameTypeDescription

frames

array of 3D point clouds

Required. List of 3D point cloud frames in the sequence.

Multi-sensor sequence

{
  "sensors": [
    {
      "name": "Lidar", 
      "task_type": "pointcloud-cuboid-sequence",
      "attributes": { ... }
    },
    {
      "name": "Camera 1", 
      "task_type": "image-vector-sequence",
      "attributes": { ... } 
    },
    ...
  ]
}
NameTypeDescription

sensors

array of sensors

Required. List of the sensors that can be labeled.

Sensor

NameTypeDescription

name

string

Required. The name of the sensor.

task_type

string

Required. The task type of the sensor. Currently, pointcloud-cuboid-sequence and image-vector-sequence are supported.

attributes

object

Required. The sample attributes for the sensor. Currently, 3D point cloud sequence and image sequence are supported.

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