Maps have long been a favored tool for navigation in both physical and virtual environments. As a navigation aid in virtual reality, map content and appearance can differ significantly. In this paper, three mini-maps ...
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Proficient training in diagnostic Imaging (dI) is crucial for veterinary students. However, the training process faces challenges, including limited access to animals and strict radiation safety regulations. Can Mixed...
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As more applications of large language models (LLMs) for 3d content in immersive environments emerge, it is crucial to study user behavior to identify interaction patterns and potential barriers to guide the future de...
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ISBN:
(数字)9798331536459
ISBN:
(纸本)9798331536466
As more applications of large language models (LLMs) for 3d content in immersive environments emerge, it is crucial to study user behavior to identify interaction patterns and potential barriers to guide the future design of immersive content creation and editing systems which involve LLMs. In an empirical user study with 12 participants, we combine quantitative usage data with post-experience questionnaire feedback to reveal common interaction patterns and key barriers in LLM-assisted3d scene editing systems. We identify opportunities for improving natural language interfaces in 3ddesign tools and propose design recommendations. Through an empirical study, we demonstrate that LLM-assisted interactive systems can be used productively in immersive environments.
Robotic micromanipulation systems (RMS) enable precise and repeatable operations under a microscope. Traditional RMS rely solely on microscopic visual feedback, necessitating time-consuming manual positioning to bring...
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Robotic micromanipulation systems (RMS) enable precise and repeatable operations under a microscope. Traditional RMS rely solely on microscopic visual feedback, necessitating time-consuming manual positioning to bring the tool tip within the microscope field-of-view (Micro-FOV), which limits efficiency and heavily depends on operator skill. This paper proposes an innovative RMS that integrates macro and micro vision to automate the aforementioned tool tip positioning and facilitate resilient control. The system utilizes an external camera to obtain the macro field-of-view (Macro-FOV), containing the tool and fiducial markers, and estimates the tool tip's 3d position by triangulation. Visual servoing is then used to guide the tool tip towards the Micro-FOV. Under the Micro-FOV, a tool-sweep detector based on partitioneddifference images is used to sequentially locate the tool's shaft and tip. After auto-focusing, the system executes tool tip and Petri dish resilient control based on our developed self-calibration and self-recalibration mechanisms. during the operation, the system provides an intuitive user interface that includes both macro and micro information, improving the visualization and productivity of micromanipulation. Experiments show that the self-initialization scheme can be implemented across different macro camera viewpoints, reducing the average tip positioning time from 65.70 to 50.08 seconds compared to manual operation, thereby decreasing manual labor intensity and improving efficiency. The self-recalibration mechanism achieves precision and resilient control, with an average error of 0.95 mu m over 25 continuous trials. Additionally, the system exhibits robustness against vibration and visual interference, underscoring its potential for diverse biomedical applications.
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