  We explore the application of volumetric reconstruction from structured-light
sensors in cognitive neuroscience, specifically in the quantification of the
size-weight illusion, whereby humans tend to systematically perceive smaller
objects as heavier. We investigate the performance of two commercial
structured-light scanning systems in comparison to one we developed
specifically for this application. Our method has two main distinct features:
First, it only samples a sparse series of viewpoints, unlike other systems such
as the Kinect Fusion. Second, instead of building a distance field for the
purpose of points-to-surface conversion directly, we pursue a first-order
approach: the distance function is recovered from its gradient by a screened
Poisson reconstruction, which is very resilient to noise and yet preserves
high-frequency signal components. Our experiments show that the quality of
metric reconstruction from structured light sensors is subject to systematic
biases, and highlights the factors that influence it. Our main performance
index rates estimates of volume (a proxy of size), for which we review a
well-known formula applicable to incomplete meshes. Our code and data will be
made publicly available upon completion of the anonymous review process.
