CAREER: Reconstructing 3D Models from Today?s Scanning Devices

Johns Hopkins University, Baltimore MD

Investigators

Abstract

Abstract PI ? Kazhdan, Michael Advances in 3D scanning technology have provided an essential means for acquiring information about surfaces and shapes in the real world and have played an important role in fields ranging from the humanities (e.g. cultural preservation) to the sciences (e.g. medical imaging). This research contributes to this trend by developing algorithms for reconstructing 3D models from the raw data returned by today's acquisition modalities. The challenges focused on in this work are two-fold: First, to be useful in practice, the algorithms must be able to process huge datasets, often too large to be able to fit into the working memory of an application. Second, the algorithms must be designed to be versatile, capable of adapting to the varying and non-uniform data returned by the different scanners. To address these challenges, the investigators reduce the problem of surface reconstruction to the solution of a Poisson equation which can be solved over an octree adapted to the scanned data. Specifically, this research makes three separate contributions. First, it describes a novel algorithm for solving the global Poisson system in a local manner, providing a solver that only requires a small subset of the system to be maintained in working memory at any given time. Second, it presents a novel data-structure that enables streaming traversal through an octree, making it possible to process high-resolution data that is too large to fit into memory. And third, using a finite elements formulation, it generalizes a method for model fitting that allows functions to be fit to data sampled using non-point primitives, extending the breadth of acquisition modalities supported by the reconstruction algorithm.