Self-supervised cross frame-rate event-frame depth learning without ground truth

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 alignment, temporal instability, and inefficient fusion of event and frame modalities. To overcome these limitations, this study proposes a novel Self-Supervised Event-Frame Self-Attention Transformer (EF-SAT) framework for depth estimation without ground-truth depth supervision. The proposed method employs a Vision Transformer-based encoder to jointly model event streams and intensity frames, while a cross-frame-rate self-attention mechanism captures spatiotemporal dependencies across heterogeneous data rates. The framework further carries revolutionary self-sampling based primarily on bilinear interpolation, timescale-consistent intensity decoding, and ego-motion optimization that uses absolute trajectory error (ATE) and relative pose error (RPE) constraints to improve geometric balances in successive frames. Depth and pose are learned using photometric reconstruction, event-frame alignment, temporal consistency, and smoothness constraints in a fully self-supervised manner. Experiments conducted on the DAVIS-240 C event-frame dataset demonstrate that the proposed approach achieves superior performance, attaining an Absolute Relative Error of 0.114 and RMSE of 0.163, outperforming state-of-the-art deep learning baselines. Overall, the proposed EF-SAT model demonstrates robust, temporally stable, and accurate depth estimation in dynamic scenes, highlighting its potential for real-world applications such as robotics, autonomous navigation, and event-driven perception systems.