The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. However, the scarcity of high-quality data and limited collaboration between industry and academia have constrained progress in this field. Building on recent advancements in 3D reconstruction, we host the MetaFood Workshop and its challenge for Physically Informed 3D Food Reconstruction. This challenge focuses on reconstructing volume-accurate 3D models of food items from 2D images, using a visible checkerboard as a size reference. Participants were tasked with reconstructing 3D models for 20 selected food items of varying difficulty levels: easy, medium, and hard. The easy level provides 200 images, the medium level provides 30 images, and the hard level provides only 1 image for reconstruction. In total, 16 teams submitted results in the final testing phase. The solutions developed in this challenge achieved promising results in 3D food reconstruction, with significant potential for improving portion estimation for dietary assessment and nutritional monitoring. More details about this workshop challenge and access to the dataset can be found at https://sites.google.com/view/cvpr-metafood-2024.

翻译：随着计算机视觉在营养与膳食监测领域应用兴趣的日益增长，针对食物项的高级三维重建技术得到了发展。然而，高质量数据的稀缺以及工业界与学术界合作的有限性制约了该领域的进展。基于三维重建领域的最新进展，我们举办了MetaFood研讨会及其"基于物理信息的3D食物重建"挑战赛。本次挑战赛的重点是从二维图像重建具有精确体积的食物三维模型，并使用可见的棋盘格作为尺寸参考。参赛者的任务是为20个选定的、难度各异的食物项重建三维模型，难度分为简单、中等和困难三个等级。简单等级提供200张图像，中等等级提供30张图像，而困难等级仅提供1张图像用于重建。总计有16支团队在最终测试阶段提交了结果。本次挑战赛中开发的解决方案在3D食物重建方面取得了令人瞩目的成果，对于改进膳食评估和营养监测中的份量估算具有巨大潜力。有关本次研讨会挑战赛的更多详情及数据集访问，请访问 https://sites.google.com/view/cvpr-metafood-2024。