Today, computer vision algorithms show near-perfect performance, better than human when there are clear, well curated and enough amount of data. However, there remains a substantial gap when it comes to applying state-of-the-art computer vision algorithms to food data, particularly when dealing with food in its natural, uncontrolled environment, often referred to as “data in the wild.” This gap stems from the inherent challenges in noisy, watermarked, and low-quality food data readily available on the internet. The MetaFood Workshop (MTF) invites the CVPR community to engage with the food domain-related challenges. These challenges provide not only a demanding, real testing environment for the development of robust computer vision algorithms, but also an exciting opportunity to develop new algorithms in the fields of food data analysis and food digitization.

The aim of this challenge is to highlight the importance of accurate 3D food reconstruction through a monocular camera, especially from a single image. This precise 3D modeling serves crucial purposes in food computing, such as keeping dietary records, necessitating an accurate representation of food portion sizes.