VolETA: One- and Few-shot Food Volume Estimation
arxiv(2024)
摘要
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
precision of volume assessments and significantly contributes to computational
nutrition and dietary monitoring advancements.
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