Monocular Video Sequence

Monocular video sequence analysis focuses on extracting 3D information, such as depth and camera pose, from a single video stream without relying on stereo vision or depth sensors. Current research emphasizes self-supervised learning methods, often employing convolutional neural networks (CNNs) or transformer-based architectures like Swin Transformers, to improve accuracy and efficiency while reducing model size. These advancements are driving progress in applications like autonomous driving, robotics, and augmented reality by enabling robust scene understanding and 3D reconstruction from readily available monocular video data. The development of unified frameworks that jointly estimate multiple scene properties, such as depth, optical flow, and camera pose, is also a significant trend.