Depth Fusion

Depth fusion integrates data from multiple depth sources, aiming to improve the accuracy, robustness, and completeness of depth maps for various applications. Current research focuses on developing efficient fusion algorithms and architectures, such as transformer-based networks and those leveraging multi-scale or multi-view information, often incorporating techniques like attentive mechanisms and uncertainty estimation to handle noise and inconsistencies. These advancements are significantly impacting fields like autonomous driving, augmented reality, and 3D reconstruction by enabling more reliable and detailed 3D scene understanding from diverse sensor modalities.