Abstract
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.
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Acknowledgements
We would like to thank Masatoshi Honda and Norihito Shirasaki from the Naraha Center for Remote Control Technology Development of Japan Atomic Energy Agency for helping us measure the camera pose. We appreciate their kindness. This work was supported by the Nuclear Energy Science & Technology and Human Resource Development Project (through concentrating wisdom) from the Japan Atomic Energy Agency/Collaborative Laboratories for Advanced Decommissioning Science.
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Matsumoto, T., Hanari, T., Kawabata, K. et al. Estimated camera trajectory-based integration among local 3D models sequentially generated from image sequences by SfM–MVS. Artif Life Robotics 29, 358–371 (2024). https://doi.org/10.1007/s10015-024-00949-4
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DOI: https://doi.org/10.1007/s10015-024-00949-4