3D reconstruction from monocular images is a rapidly evolving research area in computer vision with significant applications in food image analysis. The ability to reconstruct 3D food models from single 2D eating occasion images in real-world physical units allows users to share food experiences in three dimensions and provides crucial information about food portions, facilitating the tracking of individual nutrition intake. However, 3D reconstruction presents unique challenges that make it particularly valuable to evaluate the robustness and capability of existing computer vision algorithms. Specifically, food items in in-the-wild images exhibit a wide variety of colors, textures, shapes, and sizes. Their reflective properties can vary drastically, from the glossy surface such as a boiled egg to the matte exterior such as yeast bread. Furthermore, when multiple food items are placed together, occlusions, overlapping, and shadows introduce complex scenarios that a robust algorithm needs to extract useful information. Additionally, food is rarely uniform where the same dish can be presented in multiple ways, making approaches using predefined 3D models or shapes as references insufficient. Therefore, evaluating CV/AI algorithms on 3D food reconstruction pushes the boundaries of current technologies and encourages them to handle high intra-class variability and inter-class data with complex spatial arrangements. 

CVPR2025 MetaFood workshop site:
https://sites.google.com/view/cvpr-metafood-2025/

**The deadline of this competition has been extended to May 11 2025**