Abstract
This paper proposes a model-based method for texture mapping using close-range images and Lidar point clouds. Lidar point clouds are used to aid occlusion detection. For occluded areas, we compensate the occlusion by different view-angle images. Considering the authenticity of façade with repeated patterns under different illumination conditions, a selection of optimum pattern is suggested. In the selection, both geometric shape and texture are analyzed. The grey level co-occurrence matrix analysis is applied for the selection of the optimal façades texture to generate of photorealistic building models. Experimental results show that the proposed method provides high fidelity textures in the generation of photorealistic building models. It is demonstrated that the proposed method is also practical in the selection of the optimal texture.
Highlights
Regarding façade textures, building models can be divided into three categories: block, generic, and photorealistic models
Close-range images provide a wealth of façade information with high spatial resolution for texture mapping
Lidar point clouds with accurate 3D information provide a good way for occlusion detection
Summary
Regarding façade textures, building models can be divided into three categories: block, generic, and photorealistic models. Close-range images provide a wealth of façade information with high spatial resolution for texture mapping. The façade may be occluded by other objects, such as trees, cars, and so on, in image acquisition. Those occluded parts would cause the façade images unreal. In this reason, it’s a major process to detect and compensate occlusion parts. Lidar point clouds with accurate 3D information provide a good way for occlusion detection. Paper proposes a model-based method for texture mapping using close-range images and Lidar point clouds. Considering the authenticity of façade with repeated patterns under different illumination conditions, a selection of optimum pattern is implemented to yield a realistic model
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