In the paper, we propose a novel object-oriented hierarchy radiation consistency method for dense matching of different temporal and different sensor data in the 3D reconstruction. For different temporal images, our illumination consistency method is proposed to solve both the illumination uniformity for a single image and the relative illumination normalization for image pairs. Especially in the relative illumination normalization step, singular value equalization and linear relationship of the invariant pixels is combined used for the initial global illumination normalization and the object-oriented refined illumination normalization in detail, respectively. For different sensor images, we propose the union group sparse method, which is based on improving the original group sparse model. The different sensor images are set to a similar smoothness level by the same threshold of singular value from the union group matrix. Our method comprehensively considered the influence factors on the dense matching of the different temporal and different sensor stereoscopic image pairs to simultaneously improve the illumination consistency and the smoothness consistency. The radiation consistency experimental results verify the effectiveness and superiority of the proposed method by comparing two other methods. Moreover, in the dense matching experiment of the mixed stereoscopic image pairs, our method has more advantages for objects in the urban area.
Keywords: dense matching in 3D reconstruction; different temporal and different sensor images; illumination consistency; smoothness consistency.