Visual Estimation

Visual estimation research focuses on accurately determining various quantities from visual data, ranging from robot pose and configuration to human body measurements and building elevations. Current efforts utilize diverse approaches, including deep learning models (e.g., convolutional neural networks, factorization machines), statistical methods (e.g., influence functions, Monte Carlo methods), and geometric algorithms, often incorporating techniques like image segmentation and trajectory analysis. These advancements have significant implications for robotics, urban planning, healthcare (e.g., pain assessment), and personalized user experiences in gaming, improving automation and enabling more accurate, data-driven decision-making across various fields.