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This paper describes a method for integrating multiple dense point clouds using a shared landmark to generate a single real-scale integrated result for photogrammetry. It is difficult to integrate high-density point clouds reconstructed by photogrammetry because the scale differs with each photogrammetry. To solve this problem, this study places a QR code of known sizes, which is a shared landmark, in the reconstruction target environment and divides the reconstruction target environment based on the position of the QR code that is placed. Then, photogrammetry is performed for each divided environment to obtain each high-density point cloud. Finally, we propose a method of scaling each high-density point cloud based on the size of the QR code and aligning each high-density point cloud as a single high-point cloud by partial-to-partial registration. To verify the effectiveness of the method, this paper compares the results obtained by applying all images to photogrammetry with those obtained by the proposed method in terms of accuracy and computation time. In this verification, ideal images generated by simulation and images obtained in real environments are applied to photogrammetry. We clarify the relationship between the number of divided environments, the accuracy of the reconstruction result, and the computation time required for the reconstruction. [ABSTRACT FROM AUTHOR]

This paper describes a three-dimensional (3D) modeling method for sequentially and spatially understanding situations in unknown environments from an image sequence acquired from a camera. The proposed method chronologically divides the image sequence into sub-image sequences by the number of images, generates local 3D models from the sub-image sequences by the Structure from Motion and Multi-View Stereo (SfM–MVS), and integrates the models. Images in each sub-image sequence partially overlap with previous and subsequent sub-image sequences. The local 3D models are integrated into a 3D model using transformation parameters computed from camera trajectories estimated by the SfM–MVS. In our experiment, we quantitatively compared the quality of integrated models with a 3D model generated from all images in a batch and the computational time to obtain these models using three real data sets acquired from a camera. Consequently, the proposed method can generate a quality integrated model that is compared with a 3D model using all images in a batch by the SfM–MVS and reduce the computational time. [ABSTRACT FROM AUTHOR]

Photogrammetry is a three-dimensional (3D) reconstruction from images. In photogrammetry, when each image captures the features of the target object for 3D reconstruction, a more highly accurate 3D reconstruction can be obtained in a shorter time from a small number of images. For this reason, effective images for 3D reconstruction must be selected. In this paper, we generate test images by constructing a virtual environment and changing the shooting conditions based on the simulation for this image selection. In particular, we focus on linear trajectory shooting, in which the camera moves on a straight rail and obtains images. We verify the 3D reconstruction considering the calculation time reduction based on effective image selection. The experimental result shows that the camera pose estimation can be improved using images obtained at multiple shooting angles in the case of linear trajectory shooting. The additional experimental result reveals that the calculation time for the reconstruction result is reduced by applying the images selected at regular intervals. [ABSTRACT FROM AUTHOR]