O.P. Heijl
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Multivariate volumetric datasets are becoming increasingly large and complex, making transfer function design for direct volume rendering more difficult. Recent work has shown that flat dimensionality reduction techniques, such as t-SNE, can support transfer function design by projecting high-dimensional voxel attributes into a two-dimensional embedding space. However, flat dimensionality reduction methods become difficult to scale to datasets containing millions of voxels. They produce visually cluttered transfer function domains and require large nearest-neighbor structures for mapping rendering samples to the embedding. In this work, we use Hierarchical Stochastic Neighbor Embedding (HSNE) as a scalable alternative for dimensionality reduction-based transfer function design in multivariate volume rendering. Instead of defining the transfer function over all voxels, we select a level of the HSNE hierarchy and use its landmarks as a reduced domain, and integrate this representation into the rendering pipeline. Our method is implemented and evaluated in the ManiVault framework using large multivariate tissue datasets. The results show that the HSNE-based approach significantly reduces preprocessing and rendering times compared to a t-SNE-based baseline, while also reducing visual clutter in the transfer function space. Higher hierarchy levels further improve runtime performance and simplify interaction, although they may lose fine detail. These results demonstrate that hierarchical dimensionality reduction can improve the scalability and usability of dimensionality reduction-based transfer function design for large multivariate volumetric datasets.
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Multivariate volumetric datasets are becoming increasingly large and complex, making transfer function design for direct volume rendering more difficult. Recent work has shown that flat dimensionality reduction techniques, such as t-SNE, can support transfer function design by projecting high-dimensional voxel attributes into a two-dimensional embedding space. However, flat dimensionality reduction methods become difficult to scale to datasets containing millions of voxels. They produce visually cluttered transfer function domains and require large nearest-neighbor structures for mapping rendering samples to the embedding. In this work, we use Hierarchical Stochastic Neighbor Embedding (HSNE) as a scalable alternative for dimensionality reduction-based transfer function design in multivariate volume rendering. Instead of defining the transfer function over all voxels, we select a level of the HSNE hierarchy and use its landmarks as a reduced domain, and integrate this representation into the rendering pipeline. Our method is implemented and evaluated in the ManiVault framework using large multivariate tissue datasets. The results show that the HSNE-based approach significantly reduces preprocessing and rendering times compared to a t-SNE-based baseline, while also reducing visual clutter in the transfer function space. Higher hierarchy levels further improve runtime performance and simplify interaction, although they may lose fine detail. These results demonstrate that hierarchical dimensionality reduction can improve the scalability and usability of dimensionality reduction-based transfer function design for large multivariate volumetric datasets.
Natural disasters often present significant challenges for rescue operations due to the complex and hazardous environments they create. Teleoperation technology, particularly haptic bilateral teleoperation, offers promising solutions to enhance the efficiency, safety, and effectiveness of disaster response. This study explores the feasibility of approximating dynamic object movement to achieve satisfactory force feedback for haptic bilateral teleoperation systems, focusing on minimizing the impact of network delays on user experience. We conducted a series of experiments and user studies to assess the system’s performance under various delay conditions. Our findings indicate that dynamic object movement can be successfully approximated to provide realistic force feedback. The user study revealed that network delays greater than 75 ms significantly impact task difficulty, and delays beyond 125 ms affect system usability, although the system remains functional up to a total delay of 200 ms. The acceptable total delay, including system latency of approximately 135 ms, is found to be around 185 ms for optimal user experience. This research contributes to the development of teleoperation systems by providing insights into the
acceptable levels of network delay and the practical implementation of force feedback mechanisms, paving the way for future advancements in the field. ...
acceptable levels of network delay and the practical implementation of force feedback mechanisms, paving the way for future advancements in the field. ...
Natural disasters often present significant challenges for rescue operations due to the complex and hazardous environments they create. Teleoperation technology, particularly haptic bilateral teleoperation, offers promising solutions to enhance the efficiency, safety, and effectiveness of disaster response. This study explores the feasibility of approximating dynamic object movement to achieve satisfactory force feedback for haptic bilateral teleoperation systems, focusing on minimizing the impact of network delays on user experience. We conducted a series of experiments and user studies to assess the system’s performance under various delay conditions. Our findings indicate that dynamic object movement can be successfully approximated to provide realistic force feedback. The user study revealed that network delays greater than 75 ms significantly impact task difficulty, and delays beyond 125 ms affect system usability, although the system remains functional up to a total delay of 200 ms. The acceptable total delay, including system latency of approximately 135 ms, is found to be around 185 ms for optimal user experience. This research contributes to the development of teleoperation systems by providing insights into the
acceptable levels of network delay and the practical implementation of force feedback mechanisms, paving the way for future advancements in the field.
acceptable levels of network delay and the practical implementation of force feedback mechanisms, paving the way for future advancements in the field.