VolETA: One- and Few-shot Food Volume Estimation

TL;DR

VolETA introduces a novel 3D generative approach for accurate food volume estimation from minimal RGBD images, improving dietary assessment tools with high precision and robustness.

Contribution

This paper presents a new methodology combining 3D mesh reconstruction, scaling, and deep learning models for one- and few-shot food volume estimation, addressing occlusions and complex geometries.

Findings

Achieves 10.97% MAPE on the MTF dataset

Effectively handles occlusions and lighting variations

Provides robust volume estimates for complex foods

Abstract

Accurate food volume estimation is essential for dietary assessment, nutritional tracking, and portion control applications. We present VolETA, a sophisticated methodology for estimating food volume using 3D generative techniques. Our approach creates a scaled 3D mesh of food objects using one- or few-RGBD images. We start by selecting keyframes based on the RGB images and then segmenting the reference object in the RGB images using XMem++. Simultaneously, camera positions are estimated and refined using the PixSfM technique. The segmented food images, reference objects, and camera poses are combined to form a data model suitable for NeuS2. Independent mesh reconstructions for reference and food objects are carried out, with scaling factors determined using MeshLab based on the reference object. Moreover, depth information is used to fine-tune the scaling factors by estimating the potential volume range. The fine-tuned scaling factors are then applied to the cleaned food meshes for accurate volume measurements. Similarly, we enter a segmented RGB image to the One-2-3-45 model for one-shot food volume estimation, resulting in a mesh. We then leverage the obtained scaling factors to the cleaned food mesh for accurate volume measurements. Our experiments show that our method effectively addresses occlusions, varying lighting conditions, and complex food geometries, achieving robust and accurate volume estimations with 10.97% MAPE using the MTF dataset. This innovative approach enhances the precision of volume assessments and significantly contributes to computational nutrition and dietary monitoring advancements.