Understanding sensor data can be challenging for non-experts because of the complexity and unique semantic meanings of sensor modalities. This calls for intuitive and effective methods to present sensor information. However, creating intuitive sensor data visualizations presents three key challenges: the variability of sensor readings, gaps in domain comprehension, and the dynamic nature of sensor data. To address these issues, we develop Vivar, a novel AR system that integrates multi-modal sensor data and presents 3D volumetric content for visualization. In particular, we introduce a cross-modal embedding approach that maps sensor data into a pre-trained visual embedding space through barycentric interpolation. This allows for accurate and continuous integration of multi-modal sensor information. Vivar also incorporates sensor-aware AR scene generation using foundation models and 3D Gaussian Splatting (3DGS) without requiring domain expertise. In addition, Vivar leverages latent reuse and caching strategies to accelerate 2D and AR content generation. Our extensive experiments demonstrate that our system achieves 11$\times$ latency reduction without compromising quality. A user study involving over 485 participants, including domain experts, demonstrates Vivar's effectiveness in accuracy, consistency, and real-world applicability, paving the way for more intuitive sensor data visualization.

翻译：对于非专业人士而言，理解传感器数据可能具有挑战性，这源于传感器模态的复杂性及其独特的语义含义。这需要直观且有效的方法来呈现传感器信息。然而，创建直观的传感器数据可视化面临三个关键挑战：传感器读数的可变性、领域理解上的差距以及传感器数据的动态特性。为解决这些问题，我们开发了Vivar，一个新颖的增强现实（AR）系统，它集成了多模态传感器数据，并呈现用于可视化的3D体内容。具体而言，我们引入了一种跨模态嵌入方法，通过重心插值将传感器数据映射到预训练的视觉嵌入空间中。这使得多模态传感器信息能够被准确且连续地整合。Vivar还结合了基于基础模型和3D高斯泼溅（3DGS）的传感器感知AR场景生成，无需领域专业知识。此外，Vivar利用潜在重用和缓存策略来加速2D和AR内容的生成。我们的大量实验表明，我们的系统在不牺牲质量的情况下实现了11倍的延迟降低。一项涉及超过485名参与者（包括领域专家）的用户研究证明了Vivar在准确性、一致性和实际应用性方面的有效性，为更直观的传感器数据可视化铺平了道路。