VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation
Journal:
arXiv
Published Date:
May 15, 2025
Abstract
Accurate food volume estimation is crucial for medical nutrition management
and health monitoring applications, but current food volume estimation methods
are often limited by mononuclear data, leveraging single-purpose hardware such
as 3D scanners, gathering sensor-oriented information such as depth
information, or relying on camera calibration using a reference object. In this
paper, we present VolE, a novel framework that leverages mobile device-driven
3D reconstruction to estimate food volume. VolE captures images and camera
locations in free motion to generate precise 3D models, thanks to AR-capable
mobile devices. To achieve real-world measurement, VolE is a reference- and
depth-free framework that leverages food video segmentation for food mask
generation. We also introduce a new food dataset encompassing the challenging
scenarios absent in the previous benchmarks. Our experiments demonstrate that
VolE outperforms the existing volume estimation techniques across multiple
datasets by achieving 2.22 % MAPE, highlighting its superior performance in
food volume estimation.
