A method for automatic generating LOD2 building models based on energy function primitive extraction
In recent years,with the rapid development of 3D real scene(3DRS),the reconstruction of 3D models of buildings has become an important part of smart city construction. This paper proposes a fully automatic framework for generating LOD2 building models,aimed at addressing the challenges of generating LOD2 building models for city-level 3DRS applications. In order to automatically extract building objects in large scenes,this study utilizes orthophoto images and acquires building boundary information through image segmentation. Subsequently,an enhanced plane region growing method is employed to extract high-quality segmented planes,with the results being optimized via a mixed linear model capable of better addressing issues like local damage and reconstruction errors in urban buildings. The experimental results indicate that the proposed method generates higher quality LOD2 building models and adeptly manages complex scenarios in urban building modeling,yielding more robust reconstruction outcomes.
level of detail (LOD)building reconstructionregion segmentationautomatic generatingprimitive extraction
万方数据
维普
