Abstract
Acquiring realistic texture details for 3D models is important in 3D reconstruction. However, the existence of geometric errors, caused by noisy RGB-D sensor data, always
makes the color images cannot be accurately aligned onto reconstructed 3D models. In this paper, we propose a
global-to-local correction strategy to obtain more desired
texture mapping results. Our algorithm first adaptively selects an optimal image for each face of the 3D model,
which can effectively remove blurring and ghost artifacts
produced by multiple image blending. We then adopt a nonrigid global-to-local correction step to reduce the seaming
effect between textures. This can effectively compensate for
the texture and the geometric misalignment caused by camera pose drift and geometric errors. We evaluate the proposed algorithm in a range of complex scenes and demonstrate its effective performance in generating seamless high
fidelity textures for 3D models