Using Message Converter Extensions to Display 3D Markers in Foxglove Studio

Write a message converter to visualize custom markers in the 3D panel
Esther WeonEsther Weon ·
Sam NosenzoSam Nosenzo ·
5 min read
Using Message Converter Extensions to Display 3D Markers in Foxglove Studio

By writing your own message converter extension, you can use Foxglove Studio's out-of-the-box panels to visualize your custom messages – even if these messages' schemas aren't natively supported by any panel.

In this tutorial, we’ll convert custom messages to use a Foxglove-supported schema for easy in-app visualization.

Why use message converters?

Though you could build a custom panel every time you want to visualize some unique data, you can save a great deal of time and effort by simply writing a message converter instead. By transforming data into a supported format, message converters allow you to leverage Foxglove Studio's existing visualization capabilities – and not build another new bespoke tool from scratch.

For the purposes of this tutorial, let’s assume that our robots record detected_objects messages that we want to visualize as boxes in Studio’s 3D panel for easy debugging (Download example MCAP file here).

When we review the 3D panel's supported message types – which include transforms, point clouds, images, and more – we see that we can display primitive shapes like cubes using the scene entities schema.

However, we have one small problem – our messages use a custom schema to represent detected objects:

type DetectedObject = {
  position: [number, number, number];
  markerType: "robot" | "human" | "package";
  scale: [number, number, number];
  timestamp: Time;
  frameId: string;

Instead of creating an entirely new panel to visualize these detected objects, let’s write a message converter that transforms them into already-supported foxglove.SceneUpdate messages, so that they can be visualized in the existing 3D panel.

Set up your extension directory

Use create-foxglove-extension to generate an extension directory:

$ npm init [email protected] mySceneUpdateConverter

This will create a mySceneUpdateConverter directory with some template source code.

Write the converter

The index.ts file in your project's src folder is the entry point for your extension source code. It exports an activate function that accepts a single extensionContext argument of type ExtensionContext.

First, let’s add the @foxglove/schemas packages to our directory:

$ cd mySceneUpdateConverter
$ yarn add @foxglove/schemas

Then, import the following packages in the index.ts file:

import { CubePrimitive, SceneUpdate } from "@foxglove/schemas";
import { Time } from "@foxglove/schemas/schemas/typescript/Time";
import { ExtensionContext } from "@foxglove/studio";

And define our detected_object schema as a TypeScript type:

type DetectedObject = {
  position: [number, number, number];
  markerType: "robot" | "human" | "package";
  scale: [number, number, number];
  timestamp: Time;
  frameId: string;

To register a message converter, we call registerMessageConverter on the extensionContext argument with three arguments: the source schema name (detected_object), the destination schema name (foxglove.SceneUpdate), and the converter function that will do the transformation:

export function activate(extensionContext: ExtensionContext) {
    fromSchemaName: "detected_object",
    toSchemaName: "foxglove.SceneUpdate",
    converter: (inputMessage: DetectedObject): SceneUpdate => {
      // logic to turn detected_object messages into foxglove.SceneUpdate messages

Fill out the converter function to re-map our detected_object messages’ fields to the foxglove.SceneUpdate schema’s fields. We'll display all detected objects as colored cubes – blue for robots, red for humans, and green for packages.

  1. how we are going to visualize different detected objects as differently colored cube markers, and 2) how we need to translate this custom way of representing detected objects as 3D markers (in this case, cubes)
converter: (inputMessage: MySchema) => {
  const { position, scale, markerType, timestamp, frameId } = inputMessage;
  const colorMap = {
    robot: { r: 0, g: 0, b: 1, a: 1 },
    human: { r: 1, g: 0, b: 0, a: 1 },
    package: { r: 0, g: 1, b: 0, a: 1 },

  const cubePrimitive: CubePrimitive = {
    pose: {
      position: { x: position[0], y: position[1], z: position[2] },
      orientation: { x: 0, y: 0, z: 0, w: 1 },
    size: { x: scale[0], y: scale[1], z: scale[2] },
    color: colorMap[markerType] || { r: 1, g: 1, b: 1, a: 1 },

  const sceneUpdateMessage = {
    deletions: [],
    entities: [
        id: "detectedObjects-entities",
        frame_id: frameId,
        lifetime: { sec: 10, nsec: 0 },
        frame_locked: false,
        metadata: [],
        arrows: [],
        cubes: [cubePrimitive],
        spheres: [],
        cylinders: [],
        lines: [],
        triangles: [],
        texts: [],
        models: [],

  return sceneUpdateMessage;

Test the extension

To build and install your extension for local testing in the Foxglove Studio desktop app, run the following command in the extension directory:

$ yarn local-install

In the Studio desktop app, open the Extensions tab in the app sidebar – you'll now see mySceneUpdateConverter appear in the list of installed extensions:

extensions sidebar

Connect to your data source, and add a 3D panel to the layout. Once you toggle on the detected_objects topic in the panel's settings, you should see your custom SceneEntity messages visualized in the scene:

3D panel

Release the extension

Once you are happy with how your extension works, you can package it and share with your team using the foxglove CLI tool. This will deploy the extension to all users within your organization.

$ yarn package
$ foxglove extensions publish ./my-extension.1.0.0.foxe

Share your feedback

Read the extensions documentation for more details on Foxglove Studio's extension API.

As we continue building out extension support, we’d love to hear your feedback. Join the conversation in our Slack community or on Twitter, or check out our GitHub to file feature requests.

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