Foot Depth Map Point Cloud Completion using Deep Learning with Residual Blocks

Nolan Lunscher, John Zelek


Fit is extremely important in footwear as fit largely determines performance
and comfort. Current footwear fit estimation mainly uses
only shoe size, which is extremely limited in characterizing the
shape of a foot or the shape of a shoe. 3D scanning presents a
solution to this, where a foot shape can be captured and virtually
fit with shoe models. Traditional 3D scanning techniques have their
own complications however, stemming from their need to collect
views covering all aspects of an object. In this work we explore a
deep learning technique to compete a foot scan point cloud from
information contained in a single depth map view. We examine the
benefits of implementing residual blocks in architectures for this application,
and find that they can improve accuracies while reducing
model size and training time.

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