# VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction

arXiv: https://arxiv.org/abs/2506.02895 · HTML: https://mlsanity.com/paper/2506.02895/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [Tracking Anything with Decoupled Video Segmentation](https://mlsanity.com/paper/2309.03903/) (2023)
- [DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models](https://mlsanity.com/paper/2302.12231/) (2023)
- [Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement](https://mlsanity.com/paper/2303.02091/) (2023)
- [MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results](https://mlsanity.com/paper/2407.09285/) (2024)
- [MSN: Efficient Online Mask Selection Network for Video Instance Segmentation](https://mlsanity.com/paper/2106.10452/) (2021)
- [VolETA: One- and Few-shot Food Volume Estimation](https://mlsanity.com/paper/2407.01717/) (2024)
- [FoodMem: Near Real-time and Precise Food Video Segmentation](https://mlsanity.com/paper/2407.12121/) (2024)

## Built on by (most cited first, 3 of 3)

- [PerBite: A Curated Diagnostic Workflow for Bite-Aware Food Volume Estimation](https://mlsanity.com/paper/2606.02021/) (2026)
- [Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images](https://mlsanity.com/paper/2606.23653/) (2026)
- [DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding](https://mlsanity.com/paper/2607.23070/) (2026)

## Abstract (arXiv)

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.