Metric Reconstruction

Metric reconstruction aims to accurately determine the size and spatial relationships within a 3D scene from limited input, such as a single image or video. Current research focuses on overcoming inherent ambiguities in monocular vision by incorporating additional data modalities, like text descriptions or audio signals, and employing techniques such as variational inference, decoupled scale recovery, and multi-modal deep learning architectures (e.g., CNNs and diffusion models). These advancements are crucial for applications requiring precise 3D understanding, including augmented reality, robotics, and human-computer interaction, where accurate metric information is essential for realistic interaction and measurement.