https://scholar.dgist.ac.kr/handle/20.500.11750/60304 Wed, 30 Sep 2026 09:51:02 GMT 2026-09-30T09:51:02Z https://scholar.dgist.ac.kr/handle/20.500.11750/60866 Title: Human-in-the-Loop Object Segmentation for 3D Gaussian Splatting via Finger-based VR Interface Author(s): Lee, Yongseok; Park, Hyunreal; Kim, Hyunsu; Ji, Harim; Yee, Dongho; Lee, Dongjun Abstract: 3D Gaussian Splatting has recently emerged as a powerful representation for photorealistic rendering and reconstruction of complex scenes. However, its practical applications in augmented/virtual reality, digital-twin, and robotics demand accurate and structurally consistent meaningful 3D segmentation, which remains a significant challenge. Existing 3D segmentation approaches, predominantly based on multiview 2D images, frequently rely on appearance-driven criteria, resulting in semantic misclassification-either incorrectly merging distinct object parts or excessively fragmenting coherent regions. Moreover, these methods significantly struggle with objects with multiple components and occluded scenes. To address these limitations, we propose an interactive human-in-the-loop segmentation framework that combines a fast optimization-based 3D segmentation algorithm with intuitive finger-based user interactions within a virtual reality environment. Our optimization-based segmentation module runs within a few seconds (tens of times faster than existing learning-based methods) providing users with real-time visual updates on current segmentation results, enabling them to refine outputs interactively by adjusting prompts and viewpoints in a human-in-the-loop manner. Our finger-based interface system allows precise 3D spatial prompting, enabling accurate and multiview consistent prompts, thereby overcoming the limitations of traditional 2D multiview prompts and segmentation. This combination significantly improves segmentation accuracy, semantic consistency, and robustness to occlusion and multipart structures, as demonstrated by experimental results showing fine-grained subpart segmentation in cluttered scenes. Fri, 31 Jul 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60866 2026-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60501 Title: Finger-based 3D human-swarm interaction interface: Design and human-subject evaluation Author(s): Heo, Jinuk; Kim, Hyunsu; Lee, Eunhak; Lee, Youngseon; Park, Hyunreal; Huh, Seokhaeng; Lee, Seongjun; Lee, Yongseok; Lee, Dongjun Abstract: Human-swarm interaction (HSI) in 3D environments faces critical challenges, including the high degrees of freedom (DOFs) of large swarms and limited operator spatial awareness. To address these issues, we introduce a novel finger-based HSI interface capable of managing 100 or more agents. The interface integrates three core interaction methods-Attraction, Repulsion, and Relaxed-mapping-along with auxiliary utilities and viewpoint controls, leveraging finger dexterity for expressive and responsive swarm manipulation. We conducted rigorous human-subject studies across three scenarios: pattern formation, collective exploration, and coordinated navigation. Results demonstrate that our interface significantly outperforms the baseline in performance and workload, primarily due to efficient, implicit viewpoint control. We also found strong evidence for scenario-dependent optimality, where the effectiveness of interaction methods varied by task demands. Scalability analysis revealed that performance in macro-management tasks remained constant regardless of swarm size, whereas micro-management tasks scaled linearly. Furthermore, while objective performance and perceived workload generally correlated, user preference sometimes diverged when performance gains were marginal, highlighting the importance of intuitiveness. This study provides empirical insights for designing adaptive, context-aware HSI systems for large-scale human-swarm collaboration. Fri, 31 Jul 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60501 2026-07-31T15:00:00Z