Improving Depth Anything V2 Robustness to Video Compression

The study explores the use of video compression as a training strategy to enhance the robustness of machine learning models for autonomous vehicle (AV) systems, focusing on depth estimation tasks sensitive to video transformations. The researchers propose treating compression as data augmentation, allowing models to learn geometric representations that withstand telematics challenges. By fine-tuning models with compressed video inputs, they achieved significant reductions in validation errors, improving model stability and geometric restoration without compromising the performance on uncompressed inputs. The approach demonstrated a notable 35.2% reduction in video size while maintaining high-fidelity depth predictions, proving that compression artifacts can be effectively neutralized, thus ensuring safe and accurate perception in AV pipelines. This methodology offers a cost-effective solution, allowing AV systems to scale efficiently by integrating compression augmentation into model training, enhancing resilience to telematics bottlenecks like video compression.