Depth Video

Depth video analysis focuses on extracting meaningful information from sequences of depth maps, aiming to improve accuracy and efficiency in various applications. Current research emphasizes developing robust models for depth estimation in challenging open-world scenarios, often employing deep learning architectures like diffusion models and transformers, alongside techniques like contrastive learning for improved feature discrimination and self-supervised learning to reduce reliance on large annotated datasets. These advancements are driving progress in diverse fields, including video inpainting detection, surgical skill assessment, and real-time applications like augmented reality, by offering advantages such as robustness to lighting variations and enhanced privacy compared to RGB video analysis.