TRSP: Texture reconstruction algorithm driven by prior knowledge of ground object types

Abstract

The texture reconstruction algorithm uses multiview images and 3D geometric surface models as data sources to establish the mapping relationship and texture consistency constraints between 2D images and 3D geometric surfaces to produce a 3D surface model with color reality. The existing algorithms still have challenges in terms of texture quality when faced with dynamic scenes with complex outdoor features and different lighting environments. In this paper, a texture reconstruction algorithm driven by prior knowledge of ground object types is proposed. First, a multiscale and multifactor joint screening strategy is constructed to generate sparse key scenes of occlusion perception. Second, globally consistent 3D semantic mapping rules and semantic similarity measures are proposed. The multiview 2D image semantic segmentation results are refined, fused, and mapped into 3D semantic category information. Then, the 3D model semantic information is introduced to construct the energy function of the prior knowledge of the ground objects, and the color of the texture block boundary is adjusted. Experimental verification and analysis are conducted using public and actual datasets. Compared with famous algorithms such as Allene, Waechter, and OpenMVS, the core indicators of texture quality of the proposed algorithm are effectively reduced by 57.14 %, 53.24 % and 50.69 %, and it performs best in terms of clarity and contrast of texture details; the effective culling rate of moving objects is about 80 %–88.9 %, the texture mapping is cleaner and the redundant calculation is significantly reduced.