2025/06/03 by Ahmad AlMughrabi, Umair Haroon, AlMughrabi, Ahmad +5 · 1 citation
Agricultural and Biological Sciences · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Food Supply Chain Traceability #Graphics (cs.GR) #Nutritional Studies and Diet
paper · pdf · doi:10.48550/arxiv.2506.02895
openalex publication_date 2025/06/03 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
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 \changethe 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.