Self-supervised cross frame-rate event-frame depth learning without ground truth (opens in new tab)

Event-based cameras provide high-temporal-resolution motion information and wide dynamic range, making them suitable for depth estimation in challenging dynamic environments. However, existing depth learning approaches often rely on ground-truth supervision, handcrafted event representations, or computationally expensive fusion strategies, limiting scalability and real-time deployment. Recent self-supervised methods partially address annotation dependency but struggle with cross-frame-rate al...