Geographic,Geometrical and Semantic Reconstruction of Urban Scene from High Resolution Oblique Aerial Images

An effective approach is proposed for 3D urban scene reconstruction in the form of point cloud with semantic labeling. Starting from high resolution oblique aerial images,our approach proceeds through three main stages: geographic reconstruction, geometrical reconstruction and semantic reconstruction. The absolute position and orientation of all the cameras relative to the real world are recovered in the geographic reconstruction stage. Then, in the geometrical reconstruction stage,an improved multi-view stereo matching method is employed to produce 3D dense points with color and normal information by taking into account the prior knowledge of aerial imagery.Finally the point cloud is classified into three classes(building,vegetation, and ground) by a rule-based hierarchical approach in the semantic reconstruction step. Experiments on complex urban scene show that our proposed 3-stage approach could generate reasonable reconstruction result robustly and efficiently.By comparing our final semantic reconstruction result with the manually labeled ground truth, classification accuracies from86.75% to 93.02% are obtained.

Multi-source data-based 3D digital preservation of large scale ancient chinese architecture:A case report

The 3D digitalization and documentation of ancient Chinese architecture is challenging because of architectural complexity and structural delicacy.To generate complete and detailed models of this architecture,it is better to acquire,process,and fuse multi-source data instead of single-source data.In this paper,we describe our work on 3D digital preservation of ancient Chinese architecture based on multi source data.We first briefly introduce two surveyed ancient Chinese temples,Foguang Temple and Nanchan Temple.Then,we report the data acquisition equipment we used and the multi-source data we acquired.Finally,we provide an overview of several applications we conducted based on the acquired data,including ground and aerial image fusion,image and LiDAR(light detection and ranging)data fusion,and architectural scene surface reconstruction and semantic modeling.We believe that it is necessary to involve multi-source data for the 3D digital preservation of ancient Chinese architecture,and that the work in this paper will serve as a heuristic guideline for the related research communities.

Scalable point cloud meshing for imagebased large-scale 3D modeling

Image-based 3D modeling is an effective method for reconstructing large-scale scenes,especially city-level scenarios.In the image-based modeling pipeline,obtaining a watertight mesh model from a noisy multi-view stereo point cloud is a key step toward ensuring model quality.However,some state-of-the-art methods rely on the global Delaunay-based optimization formed by all the points and cameras;thus,they encounter scaling problems when dealing with large scenes.To circumvent these limitations,this study proposes a scalable pointcloud meshing approach to aid the reconstruction of city-scale scenes with minimal time consumption and memory usage.Firstly,the entire scene is divided along the x and y axes into several overlapping chunks so that each chunk can satisfy the memory limit.Then,the Delaunay-based optimization is performed to extract meshes for each chunk in parallel.Finally,the local meshes are merged together by resolving local inconsistencies in the overlapping areas between the chunks.We test the proposed method on three city-scale scenes with hundreds of millions of points and thousands of images,and demonstrate its scalability,accuracy,and completeness,compared with the state-of-the-art methods.