Abstract
3D models have been widely used by spread of many available free-software. Additionally, enormous images can be easily acquired, and images are utilized for creating the 3D models recently. The creation of 3D models by using huge amount of images, however, takes a lot of time and effort, and then efficiency for 3D measurement are required. In the efficient strategy, the accuracy of the measurement is also required. This paper develops an image selection method based on network design that means surveying network construction. The proposed method uses image connectivity graph. The image connectivity graph consists of nodes and edges. The nodes correspond to images to be used. The edges connected between nodes represent image relationships with costs as accuracies of orientation elements. For the efficiency, the image connectivity graph should be constructed with smaller number of edges. Once the image connectivity graph is built, the image selection problem is regarded as combinatorial optimization problem and the graph cuts technique can be applied. In the process of 3D reconstruction, low quality images and similar images are also extracted and removed. Through the experiments, the significance of the proposed method is confirmed. It implies potential to efficient and accurate 3D measurement.
Highlights
Specifications and configurations of cameras are set in advance as project planning, which is so called as network design (Atkinson, 1996)
The network design mainly consists of zero-order design (ZOD: the datum problem), first-order design (FOD: the configuration problem), second-order design (SOD: the weight problem), and third-order design (TOD: the densification problem)
This paper presents an image selection method based on network design for huge amount of images, and shows the effectiveness of the proposed method
Summary
Specifications and configurations of cameras are set in advance as project planning, which is so called as network design (Atkinson, 1996). The image selection based on the network design will be expected to contribute improvement of efficiency for the 3D measurements and keeping of accuracy simultaneously. This paper presents an image selection method based on network design for huge amount of images, and shows the effectiveness of the proposed method. The proposed method uses image connectivity graph. In the process of 3D reconstruction, low quality images and similar images are extracted and removed. The low quality images have only small number of feature points, and the image can be specified according to the image matching result. The similar images have small baseline length compared with average distance from the images to 3D reconstructed feature points. The efficiency of the image selection will be expected
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