Because the digitalization of real objects often entails the texture mapping of the 3D geometry model

images-to-geometry registration is indispensable.The geometry of the target in close-range photogrammetry is complex

and the shooting angle of the image changes largely

which makes images-to-geometry registration difficult to process. Traditionally

a single image is registered with a geometric model by manually selecting the corresponding points. Thus for tens of images this processing can be very time consuming. For efficiency

the position and orientation of the multi-view images can be unified in one coordinate system at first. However

this process is also difficult because of the complex and uncertain background in close-range photogrammetry. To this end

we put a plane calibration board under the target while taking images

and use the camera calibration to get both

the internal and external orientation parameters; thus

the multi-view imagesare placed in one coordinate system. The unified coordinate system means when the registration parameters of one image are known

the registration parametersof all other images can be calculated through the relative positions and orientations among them. Then

at least three groups of corresponding points are manually selected

and the registration parameters are calculated by using the absolute orientation. The achieved registration parameters are not precise enough but can be used as an initial value. Finally

by making the normalized mutual information between multi-view images and the diffuse rendered maps of the geometric modelas a similarity measure

we use Powell's nonlinear optimization method to get the precise registration parameters.In the final process

we optimize all images together

while the traditional methods optimize each single image separately; the difference is we have only 7 parameters for all images

but the traditional methods have 7 parameters for each image. The experimental results show that using the calibration board we obtain robust internal and external orientation parametersof multi-view images

and after the absolute orientation process

we find obvious gaps between the images and the rendered maps. However

after a normalized mutual information based optimization process

the gaps are significantly improved.The typical total registration work takes about 10 minutes

in which there are 4 minutes for manual work and 6 minutes for computation; by contrast

the fully manual texture-mapping work by using the commercial software-Cinema4D takes nearly 4 hours. Registration between multi-view images and geometric model compared to registration between single image and geometric model

the workload of manually selecting corresponding points is greatly reduced.The errors of manually selected points can affect the accuracy of absolute orientation

but after using normalized mutual information based nonlinear optimization

we can still get precise registration parameters. The normalized mutual information based nonlinear optimization can also be used for single image-to-geometry registration

but for multi-view images the method is more robust. The optimization method has one drawback: it requires the geometric model has a high quality. This is because the normalized mutual information has a close relationship to the surface's normal vector of the geometric model. This drawback can be avoided by using high accurate 3D scanners.