Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer



+44 (0)20 7594 0806benny.lo Website




B414BBessemer BuildingSouth Kensington Campus






BibTex format

author = {Lo, FP-W and Sun, Y and Qiu, J and Lo, BPL},
doi = {10.1109/tii.2019.2942831},
journal = {IEEE Transactions on Industrial Informatics},
pages = {577--586},
title = {Point2Volume: A vision-based dietary assessment approach using view synthesis},
url = {},
volume = {16},
year = {2020}

RIS format (EndNote, RefMan)

AB - Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation.
AU - Lo,FP-W
AU - Sun,Y
AU - Qiu,J
DO - 10.1109/tii.2019.2942831
EP - 586
PY - 2020///
SN - 1551-3203
SP - 577
TI - Point2Volume: A vision-based dietary assessment approach using view synthesis
T2 - IEEE Transactions on Industrial Informatics
UR -
UR -
UR -
VL - 16
ER -