**This year's challenge will emphasize multi-food scenarios without explicit physical references. **
These changes are motivated to align the challenge more closely with real-world applications. By focusing on multi-food scenarios, we aim to address the complexity of actual eating occasions where multiple items with diverse shapes, textures, and reflective properties coexist on a plate. In addition, the removal of explicit physical references encourages the development of algorithms that can infer scale and orientation from implicit cues (such as plates, utensils, or common food items of known size) found in natural eating settings. This realistic setup not only enhances the challenge's relevance to practical applications in nutrition tracking and dietary assessment but promotes the creation of more intelligent and adaptive computer vision solutions. 

To start, we recommend you use image to 3D generation model to create a mesh. Then use metric depth estimation model to calibrate the size. See rules for the recommended external resources.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19611415%2F5a4328f13778c762d7f36b7cce45fd5a%2Fprocess.png?generation=1743727414000892&alt=media)