AUTOMATIC TEXTURE MAPPING METHOD FOR 3D MODELS TO CIRCUMVENT OCCLUSION THROUGH 3D SPATIAL THROUGH-VIEW RELATIONSHIPS BETWEEN IMAGES,MODELS, AND POINT CLOUDS
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, P.R. China
Keywords: Texture mapping, 3D reconstruction, Point clouds, Occlusion detection, MRF
Abstract. Photorealistic 3D models play an important role in various applications related to smart cities. Texture mapping plays a crucial role as the last step of 3D reconstruction. The texture of the model directly reflects the visualization and realism of the model. In complex city scenes, objects often have varying degrees of occlusion from one another. Traditional methods to circumvent occlusion by image or model data are not very effective. In this paper, we propose an automatic texture mapping method for 3D models to circumvent occlusion through 3D spatial through-view relationships between images, models, and point clouds. First of all, calculate the density and voxel resolution of the point clouds, the point clouds here can be either dense matching point clouds or laser point clouds. Voxelization is based on point clouds, voxels generated by voxelization can reflect the spatial location of the point clouds. Secondly, to determine whether there is an occlusion of voxels between the image and the model vertices based on the spatial through-view relationship between the image, the model, and the voxels, and filter out the unobstructed images. Finally, by optimizing the data items of the Markov random field, the shading function is added to the original data items for evaluating the shading condition, based on which the most suitable image is selected for texture mapping of a triangular surface of the model. Loop computation to complete the unobstructed texture mapping of the entire model. In this paper, the public test datasets released by the International Society for Photogrammetry and Remote Sensing (ISPRS) and several urban area datasets collected by ourselves are incorporated to test the performance of the proposed method. Both qualitative and quantitative cross-sectional comparisons with existing texture mapping algorithms are conducted and presented. Experimental results indicate that our method can effectively circumvent occlusion for automated texture mapping, and generate 3D models with fewer obstructed textures, which largely improves the visual quality and realism of the models.