大阪経済大学

We investigate strategy-proof rules defined over the set of single-peaked preference profiles whose range may not be an interval. We define the class of "minmax rules with a family of upper-set tie-breaking rules", and show that it is characterized by strategy-proofness. This class is broader than that of "disturbed minmax rules" introduced by Mass & oacute; and Moreno de Barreda (2011), because, under the domain of all single-peaked preferences, the alternative selected by a strategy-proof rule whose range is not an interval may be affected by asymmetric single-peaked preferences over alternatives lying outside its range.

With the growing use of digital twins and 3D city models, 3D point cloud data have become increasingly important. Such data, however, may contain privacy-sensitive objects, including people and vehicles, which poses challenges for public release and secondary use. This study proposes a sequential 2D–3D recognition framework for extracting privacy-sensitive objects by integrating 2D image recognition and 3D point cloud recognition. The proposed framework first detects candidate regions in images and associates them with the corresponding 3D point cloud through multi-view projection, after which 3D semantic segmentation is applied only to the candidate point cloud. By restricting 3D recognition to candidate regions, the proposed method suppresses background-point contamination while reducing unnecessary 3D processing. We evaluate the method using SfM-derived 3D point clouds containing people and vehicles. The results show that the proposed method achieves higher F-scores than the selected direct 2D-projection and 3D-only baselines, reflecting a better balance between precision and recall. These findings suggest that sequentially combining 2D image recognition with 3D point cloud recognition provides an effective approach for privacy-sensitive object extraction and supports the privacy-preserving publication and secondary use of digital twins and 3D city models.