用文字精准选食物，3D建模算体积
VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction
- 输入文字指定目标食物，自动分割出指定食材
- 基于神经表面重建生成高精度3D网格，体积计算准确
- 适合饮食记录、营养管理等需要精确食物量的应用
准确估计食物体积对饮食监测、医疗营养管理和进食分析至关重要。现有3D食物体积估计方法虽能精确计算体积，但缺乏对食物份量的精准选择能力。本文提出VolTex框架，通过文本引导分割实现真实场景中特定食物对象的精确选择。用户可通过文本输入指定目标食物，系统将其自动分割，并利用神经表面重建方法生成高保真3D网格，用于体积计算。在MetaFood3D数据集上的大量实验表明，该方法能有效实现食物对象的分离与重建，显著提升体积估计准确性。代码已开源：https://github.com/GCVCG/VolTex。
原文摘要 · Abstract (English)
Accurate food volume estimation is crucial for dietary monitoring, medical nutrition management, and food intake analysis. Existing 3D Food Volume estimation methods accurately compute the food volume but lack for food portions selection. We present VolTex, a framework that improves \change{the food object selection} in food volume estimation. Allowing users to specify a target food item via text input to be segmented, our method enables the precise selection of specific food objects in real-world scenes. The segmented object is then reconstructed using the Neural Surface Reconstruction method to generate high-fidelity 3D meshes for volume computation. Extensive evaluations on the MetaFood3D dataset demonstrate the effectiveness of our approach in isolating and reconstructing food items for accurate volume estimation. The source code is accessible at https://github.com/GCVCG/VolTex.
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