# Reference-Free Food Volume Estimation from Egocentric Stereo Video

arXiv: https://arxiv.org/abs/2610.12735 · HTML: https://mlsanity.com/paper/2610.12735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [Two-view 3D Reconstruction for Food Volume Estimation](https://mlsanity.com/paper/1701.03330/) (2017)
- [MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results](https://mlsanity.com/paper/2407.09285/) (2024)
- [Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception](https://mlsanity.com/paper/2604.09886/) (2026)

## Abstract (arXiv)

Current stereo vision-based approaches for food portion estimation are hindered by the user burden of using a mobile phone to capture multiple images or placing a reference object in the scene for scale calibration. As a step towards passive dietary intake monitoring, we introduce a method that uses stereo video from a wearable egocentric device to reconstruct food items in true physical scale to accurately estimate volume. By utilizing FoundationStereo for zero-shot stereo matching, SAM 2 for food segmentation, and TSDF fusion over a set of keyframes, our method yields a visually and metrically accurate 3D mesh of any plated food item from a single stereo recording. We leverage the unique offerings of wearables, such as a factory-calibrated stereo baseline and camera poses estimated on-device via visual-inertial odometry (VIO), to perform fully automatic reconstruction without a scale reference object in the scene. We show strong initial volume estimation results across five recorded food items, achieving a mean absolute percentage error of 9.1%.