Labelvisor

Event cameras record only brightness changes in individual pixels, providing lower latency perception. However, training machine learning models for event-based perception requires specialized annotation methods and datasets. Creating a high-quality event camera dataset requires labeling strategies tailored to asynchronous sensor streams. This has led to the development of DV...

The development of autonomous vehicles is one of the most important directions of the modern automotive industry and cyber-physical systems. The safe and reliable operation of such systems largely depends on the ability of on-board computing platforms to process large amounts of data from sensors, in particular cameras, lidars, radars, and inertial measurement devices, in a t...

Night navigation remains one of the greatest challenges for the development of autonomous driving systems, as standard RGB cameras largely depend on ambient lighting. In the dark hours of the day, classic computer vision faces the problem of critically low contrast and high levels of digital noise, which turns object recognition into an extremely unstable process. The situati...

The race to build physical AI is reshaping the entire AI infrastructure landscape. While generative AI was built primarily on text and language data, robotics and embodied systems require a fundamentally different type of data: a continuous stream of multimodal, real-world interaction data. From humanoid robots and autonomous vehicles to industrial automation systems, the nex...

Advanced driver assistance systems (ADAS) and autonomous driving rely on cameras, lidars, radars, and other sensors to accurately perceive their environment. These sensing systems must operate reliably in rain, snow, fog, mud, dust, condensation, and extreme weather conditions.

A dirty camera lens, ice buildup on the lidar, or dirt splatter covering the sensor degrade t...