Annotating LiDAR data

The most common deep learning tasks on LiDAR data are variants of semantic segmentation, object detection, and classification. Therefore, annotating LiDAR data is quite similar to annotating images for those tasks. In the case of object detection, the difference is that here one puts a 3D bounding box instead of a 2D one as in the case of images. As for the semantic segmentation we want to have a single label for each point in the point cloud, as we want a single label for each pixel in an image.

You might think that there's not much of a difference, hence why the LiDAR annotation should be relatively easy as in the case of image datasets. Unfortunately, that's not the case. Because of the 3D nature of the data and the inevitable 2D nature of the editor, the annotator needs to do a tremendous amount of scene navigation and viewing of angle changes in order to correctly label all the points of an object with the same class.

To mitigate these difficulties for annotators, a good amount of engineering effort is needed, which explains the sparsity of available open-source LiDAR annotation tools for point cloud data. Those few that are available support only 3D bounding box annotation, which is relatively easy to implement compared to the semantic segmentation toolset.

For semantic segmentation, selecting points with 3D bounding boxes can get as tedious as labeling points one by one. To this end, one has to implement special tooling that enables them to select a bunch of points at once in a very intuitive manner.

Having a good tool is a key component in annotating LiDAR data. In this regard, not only convenient labeling features contribute to the success of the project but also the management capabilities of the tool. As to eventually get high-quality labels for big LiDAR datasets, one should be able to manage a team of specialized annotators by setting up complex workflows with multi-stage quality assurance.

There are a handful of companies across the world that provide both LiDAR annotation services and are building their own software to support those services. Most companies provide outsourcing services with the tools that the customer has to build in-house. However, such solutions are often non-scalable for creating high-quality training data.

10 things to consider when choosing LiDAR annotation software

When selecting a LiDAR annotation software, there are several important factors to consider to ensure the software meets your needs and provides high-quality annotations for your specific application. Some of the most important aspects to consider include:

• Quality Control and Validation: The software should offer quality control and validation tools to ensure the accuracy and consistency of the annotated data. This may include features like automated error detection, auditing tools, and built-in metrics to assess the quality of the annotations.
• Integration and Compatibility: The LiDAR annotation software should be compatible with your existing tools, hardware, and software platforms. It should offer APIs or integration options to easily connect with your data storage, machine learning frameworks, or other relevant systems.
• Customization and Flexibility: The software should be adaptable to your specific requirements, allowing for the customization of annotation tools, labels, or workflows. This flexibility will enable you to tailor the software to your project's unique needs.
• Support and Documentation: Choose a software provider that offers responsive customer support and comprehensive documentation to help you troubleshoot issues, learn about new features, and get the most out of the software.

10 things to consider when choosing LiDAR annotation services

Similar to the software, when outsourcing LiDAR annotation tasks to an external workforce, there are several essential components to consider to ensure the success of your LiDAR project and the quality of the annotated data:

The LiDAR data annotation tools existing in the market currently are yet at a very early stage. Most of them don't have any smart features integrated that use the power of machine learning or deep learning models. Similar to the image tools, we envision them having smart prediction and interactive segmentation models that would operate directly on the point cloud data. There can also be LiDAR data-specific smart tools, such as ground point removal (mostly used in object detection tasks) or tracking objects across the frames of a LiDAR scene. And finally, let's end on a strong note, by stressing on SuperData and how it will offer high-quality data, which in its turn will enhance the models behind those smart features even further!