Depth Forecasting

Depth forecasting, the prediction of depth maps from image sequences or multiple views, is crucial for applications like autonomous driving and 3D scene reconstruction. Current research emphasizes improving accuracy and robustness, particularly in challenging conditions, through innovative model architectures such as hybrid fusion methods combining camera and radar data, and recurrent or transformer-based networks for temporal prediction. These advancements leverage multi-modal information and self-supervised learning to achieve state-of-the-art performance on established benchmarks, pushing the boundaries of 3D perception capabilities. The resulting improvements in depth estimation have significant implications for various fields requiring accurate 3D scene understanding.