Surface Reconstruction

Symposium on Geometry Processing
2017 Graduate School Lecture by Pierre Alliez

http://geometry.cs.ucl.ac.uk/SGP2017/?p=gradschoo #abs_surface_reconstruction

https://team.inria.fr/titane/pierre-alliez/

The technological advances of geometric measurement devices have revolutionized our ability to digitize the world in 3D. This revolution has made possible the development of new applications such as the automatic scene interpretation and the simulation of physical phenomena at the scale of entire cities.

A key issue at the heart of this revolution is that of surface reconstruction, which consists in converting the raw measurements into a computerized surface representation. The objective is to reconstruct a surface only from measurements (most often point clouds), such that the topology and the geometry of the reconstructed surface approximate well the measured physical object. Surface reconstruction is a very ill-posed (with non-unique solutions), and the diversity of the existing approaches reflects the a priori knowledge on physical surfaces (simple, smooth, piecewise smooth) and properties sought after for the reconstructed surface (watertight, intersection-free).

This course will offer an introduction to the main families of surface reconstruction methods, in terms of the assumptions used to make the problem better posed. I will then discuss the enduring problems posed by imperfect data (imprecise, sparse or incomplete). The quest for robustness has motivated variational methods and more recent approaches inspired by the notion of optimal transport. Finally, I will discuss the emerging scientific challenges relating to several novel acquisition paradigms (sensor networks, continuous digitization, community data).
Symposium on Geometry Processing
2017 Graduate School Lecture by
Recent Reference: A Survey of Surface Reconstruction from Point Clouds. Berger, Tagliasacchi, Seversky, Alliez, Guennebaud, Levine, Sharf and Silva. Computer Graphics Forum, 36 (1), 2016.