VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction
This work addresses dietary monitoring and medical nutrition management by improving food portion selection, though it is incremental as it builds on existing volume estimation methods.
The paper tackles the problem of precise food object selection in 3D food volume estimation by introducing VolTex, a framework that uses text-guided segmentation and neural surface reconstruction to isolate and reconstruct specific food items, achieving effective results on the MetaFood3D dataset.
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.