期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2015
卷号：XL-3/W3
页码：613-617
DOI：10.5194/isprsarchives-XL-3-W3-613-2015
出版社：Copernicus Publications
摘要：Despite of the popularity of Delauney structure for mesh generation, octree based approaches remain an interesting solution for a first step surface reconstruction. In this paper, we propose a generic framework for a octree cell based mesh generation. Its input is a set of Lidar-based 3D measurements or other inputs which are formulated as a set of mass functions that characterize the level of confidence on the occupancy of each octree’s leaf. The output is a binary segmentation of the space between occupied and empty areas by taking into account the uncertainty of data. To this end, the problem is then reduced to a global energy optimization framework efficiently optimized with a min-cut approach. We use the approach for producing a large scale surface reconstruction algorithm by merging data from ubiquitous sources like airborne, terrestrial Lidar data, occupancy map and extra cues. Once the surface is computed, a solution is proposed for texturing the mesh.
