With the growth in capabilities of generative models, there has been growing interest in using photo-realistic renders of common 3D food items to improve downstream tasks such as food printing, nutrition prediction, or management of food wastage. Despite 3D modelling capabilities being more accessible than ever due to the success of NeRF based view-synthesis, such rendering methods still struggle to correctly capture thin food objects, often generating meshes with significant holes. In this study, we present an optimized strategy for enabling improved rendering of thin 3D food models, and demonstrate qualitative improvements in rendering quality. Our method generates the 3D model mesh via a proposed thin-object-optimized differentiable reconstruction method and tailors the strategy at both the data collection and training stages to better handle thin objects. While simple, we find that this technique can be employed for quick and highly consistent capturing of thin 3D objects.

翻译：随着生成模型能力的提升，利用常见三维食品物品的逼真渲染来改进下游任务（如食品打印、营养预测或食品浪费管理）引起了广泛关注。尽管基于NeRF的视图合成技术的成功使得三维建模能力比以往更易获取，但此类渲染方法在处理薄型食品对象时仍存在困难，常常生成带有显著空洞的网格。在本研究中，我们提出了一种优化策略，以实现薄型三维食品模型的改进渲染，并展示了渲染质量的定性提升。我们的方法通过一种针对薄对象优化的可微重建方法生成三维模型网格，并在数据采集和训练阶段定制策略以更好地处理薄对象。尽管方法简单，但我们发现该技术可用于快速且高度一致地捕捉薄型三维对象。