IMAGE ACQUISITION AND MODEL SELECTION FOR MULTI-VIEW STEREO

Abstract

Dense image matching methods enable efficient 3D data acquisition. Digital cameras are available at high resolution, high geometric and radiometric quality and high image repetition rate. They can be used to acquire imagery for photogrammetric purposes in short time. Photogrammetric image processing methods deliver 3D information. For example, Structure from Motion reconstruction methods can be used to derive orientations and sparse surface information. In order to retrieve complete surfaces with high precision, dense image matching methods can be applied. However, a key challenge is the selection of images, since the image network geometry directly impacts the accuracy, as well as the completeness of the point cloud. Thus, the image stations and the image scale have to be selected according carefully to the accuracy requirements. Furthermore, most dense image matching solutions are based on multi-view stereo algorithms, where the matching is performed between selected pairs of images. Thus, stereo models have to be selected from the available dataset in respect to geometric conditions, which influence completeness, precision and processing time. Within the paper, the selection of images and the selection of optimal stereo models are discussed according to to photogrammetric surface acquisition using dense image matching. For this purpose, impacts of the acquisition geometry are evaluated for several datasets. Based on the results, a guideline for the acquisition of imagery for photogrammetric surface acquisition is presented. The simple and efficient capturing approach with "One panorama each step" ensures complete coverage and sufficiently redundant observations for a surface reconstruction with high precision and reliability.