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Food volume estimation is an essential step in the pipeline of dietaryassessment and demands the precise depth estimation of the food surface andtable plane. Existing methods based on computer vision require eithermulti-image input or additional depth maps, reducing convenience ofimplementation and practical significance. Despite the recent advances inunsupervised depth estimation from a single image, the achieved performance inthe case of large texture-less areas needs to be improved. In this paper, wepropose a network architecture that jointly performs geometric understanding(i.e., depth prediction and 3D plane estimation) and semantic prediction on asingle food image, enabling a robust and accurate food volume estimationregardless of the texture characteristics of the target plane. For the trainingof the network, only monocular videos with semantic ground truth are required,while the depth map and 3D plane ground truth are no longer needed.Experimental results on two separate food image databases demonstrate that ourmethod performs robustly on texture-less scenarios and is superior tounsupervised networks and structure from motion based approaches, while itachieves comparable performance to fully-supervised methods.

Partially Supervised Multi-Task Network for Single-View Dietary Assessment