@article{Payan_IC2016, title = "From stereoscopic images to semi-regular
meshes ", journal = "Signal Processing: Image Communication ", volume =
"40", number = "", pages = "97 - 110", year = "2016", note = "", issn =
"0923-5965", doi = "http://dx.doi.org/10.1016/j.image.2015.11.004", url =
"http://www.sciencedirect.com/science/article/pii/S0923596515002015",
author = "Jean-Luc Peyrot and Frédéric Payan and Marc Antonini", keywords =
"Semi-regular mesh", keywords = "3D reconstruction", keywords =
"Stereoscopy", keywords = "Acquisition", keywords = "Poisson-disk
sampling", keywords = "GPU ", abstract = "Abstract The pipeline to get the
semi-regular mesh of a specific physical object is long and fastidious: physical
acquisition (creating a dense point cloud), cleaning/meshing (creating an
irregular triangle mesh), and semi-regular remeshing. Moreover, these three
stages are generally independent, and processed successively by different
tools. To overcome this issue, we propose in this paper a new framework to
design semi-regular meshes directly from stereoscopic images. Our
semi-regular reconstruction technique first creates a base mesh by using a
feature-preserving sampling on the stereoscopic images. Afterwards, this base
mesh is passed to a coarse-to-fine meshing process to get the semi-regular
mesh of the original surface. Experimental results prove the reliability and the
accuracy of our approach in terms of shape fidelity, compactness, but also
runtime, since many steps have been parallelized on the GPU. " }