Volumetric Reconstruction Applied to Perceptual Studies of Size and Weight
This work addresses the need for accurate volumetric measurement in perceptual studies, though it appears incremental as it builds on existing structured-light scanning with specific methodological improvements.
The researchers tackled the problem of quantifying the size-weight illusion in cognitive neuroscience by comparing volumetric reconstruction methods from structured-light sensors, finding that their novel sparse-viewpoint approach with screened Poisson reconstruction produces resilient, high-quality metric reconstructions with systematic biases identified.
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.