Size Estimation

Size estimation research focuses on accurately determining the dimensions of objects from various data sources, aiming to improve robotic perception, medical diagnosis, and agricultural monitoring. Current efforts leverage deep learning architectures like YOLOv8 and ResNet-FPN, often integrated with techniques such as Kalman filtering and shape fitting, to achieve real-time performance and handle challenges like occlusions and varying data quality (e.g., RGB-D images, point clouds). These advancements have significant implications for autonomous systems, medical image analysis (e.g., colonoscopy), and precision agriculture, enabling more efficient and accurate automation in diverse fields.