Mass Estimation

Mass estimation research spans diverse fields, aiming to accurately determine the mass of objects ranging from subatomic particles to celestial bodies and everyday containers. Current approaches leverage various techniques, including deep neural networks (like those based on concentric MacLaurin spheroids or spectral transformers), gradient-boosted trees, and product-unit networks, often incorporating additional data such as spectral information, tactile feedback, or visual cues from RGB-D cameras. These advancements improve accuracy and efficiency in diverse applications, from astrophysics (e.g., determining the mass of planets and stars) to robotics (e.g., enabling safe and precise manipulation of objects) and food science (e.g., estimating calorie content from images). The development of robust and accurate mass estimation methods is crucial for advancing scientific understanding and improving technological capabilities across numerous domains.