Data annotation & labeling blog.

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Maxim Dupont

Maxim Dupont is the founder of LabelVisor, a scientist & software engineer. His hobby of writing tech reviews has become his new way of life. His belief is that together we can achieve the best world.

Panoptic Scene Segmentation

Panoptic Scene Segmentation

Panoptic scene segmentation combines semantic and instance-based segmentation, enabling simultaneous segmentation of large areas and individual objects. In the context of urban and road environments, this means the precise hierarchical marking of roadways, sidewalks, street objects such as signs, street lights, and benches. This approach provides a comprehensive understanding

Road & Lane Segmentation Annotation: Pixel-Perfect Road Boundary Labeling

Road & Lane Segmentation Annotation: Pixel-Perfect Road Boundary Labeling

To effectively train deep learning models, high-quality annotations are required that provide pixel-precise road and lane markings. Accurate lane boundary annotation enables models to correctly identify lane boundaries even in complex traffic situations — at intersections, on curved road sections, and during roadway narrowing. Correct drivable area segmentation and

Labeling Occluded Objects for Autonomous Systems

Labeling Occluded Objects for Autonomous Systems

For an autonomous car, the ability to recognize hidden objects is one of the most difficult skills that distinguishes a simple program from true intelligence. In the real world, we almost never see objects perfectly whole: a car in front obscures part of a pedestrian, a road sign hides behind

Labeling Autonomous Vehicle Data in Low Light and Infrared

Labeling Autonomous Vehicle Data in Low Light and Infrared

Labeling data for autonomous vehicles in low-light conditions and utilizing infrared radiation is a crucial step in developing safe autonomous driving systems. Night scenes present unique challenges for sensors, but traditional cameras often lose contrast and detail, and objects can remain poorly visible or distorted due to glare and

Annotating Autonomous Vehicles in Adverse Weather: Rain, Fog & Snow Scenarios

Annotating Autonomous Vehicles in Adverse Weather: Rain, Fog & Snow Scenarios

The development of autonomous vehicles is a key area of modern research in the fields of AI and computer vision. One of the primary conditions for the safe operation of such systems is the ability to accurately perceive the environment under various operating conditions. Adverse weather phenomena, particularly rain, fog,

Camera-LiDAR Calibration: Multi-Sensor Alignment for Autonomous Vehicles

Camera-LiDAR Calibration: Multi-Sensor Alignment for Autonomous Vehicles

Modern cars are often equipped with a variety of sensors, such as cameras, LiDAR, and radar, each providing unique information about the environment. The real challenge is integrating data from disparate sensors into a single, coherent map of the environment. Calibrating cameras and LiDAR is a crucial process for achieving

Labeling High-Resolution Radar Point Clouds for AV

Labeling High-Resolution Radar Point Clouds for AV

While cameras depend on lighting and LiDAR loses effectiveness due to beam scattering in water droplets or steam particles, high-resolution radars ensure stable operation in the most difficult meteorological conditions. The ability of radio waves to pass through fog, rain, and snow makes this sensor indispensable for safe movement