Beamr fine-tuned the Depth Anything V2 monocular depth estimation model on video compressed with its CABR technology, achieving a 30.7% reduction in depth estimation error for vulnerable road users and a 16.0% aggregate error drop across all object classes. These results indicate that adaptive compression can serve as a form of data augmentation that strengthens model robustness rather than degrading it.

The compression process delivered a 35.2% reduction in video file sizes relative to baseline methods, enabling significant savings in storage and networking infrastructure for machine vision teams. By reframing compression from a necessary cost to a performance asset, Beamr’s CABR tech offers both operational efficiency and improved AI model outcomes.

Machine vision development for autonomous vehicles often involves petabyte-scale video datasets, where storage and bandwidth are critical constraints. Beamr’s findings suggest that integrating CABR-compressed footage into training pipelines can reduce infrastructure expenses while enhancing depth perception on safety-critical objects, potentially accelerating adoption in AV and other video AI applications.