Title: Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering Show Chinese title

Title: 体积编码高斯：与传递函数无关的三维高斯用于体积渲染

Abstract: While HPC resources are increasingly being used to produce adaptively refined or unstructured volume datasets, current research in applying machine learning-based representation to visualization has largely ignored this type of data. To address this, we introduce Volume Encoding Gaussians (VEG), a novel 3D Gaussian-based representation for scientific volume visualization focused on unstructured volumes. Unlike prior 3D Gaussian Splatting (3DGS) methods that store view-dependent color and opacity for each Gaussian, VEG decouple the visual appearance from the data representation by encoding only scalar values, enabling transfer-function-agnostic rendering of 3DGS models for interactive scientific visualization. VEG are directly initialized from volume datasets, eliminating the need for structure-from-motion pipelines like COLMAP. To ensure complete scalar field coverage, we introduce an opacity-guided training strategy, using differentiable rendering with multiple transfer functions to optimize our data representation. This allows VEG to preserve fine features across the full scalar range of a dataset while remaining independent of any specific transfer function. Each Gaussian is scaled and rotated to adapt to local geometry, allowing for efficient representation of unstructured meshes without storing mesh connectivity and while using far fewer primitives. Across a diverse set of data, VEG achieve high reconstruction quality, compress large volume datasets by up to 3600x, and support lightning-fast rendering on commodity GPUs, enabling interactive visualization of large-scale structured and unstructured volumes. Show Chinese abstract

Abstract: 虽然HPC资源正被越来越多地用于生成自适应细化或非结构化体积数据集，但目前在将基于机器学习的表示方法应用于可视化方面的研究，很大程度上忽略了这种类型的数据。 为了解决这个问题，我们引入了体积编码高斯（VEG），这是一种针对非结构化体积的新型基于3D高斯的科学体积可视化表示方法。 与之前存储每个高斯视图相关颜色和不透明度的3D高斯点绘制（3DGS）方法不同，VEG通过仅编码标量值，将视觉外观与数据表示解耦，从而实现与传输函数无关的3DGS模型渲染，以支持交互式科学可视化。 VEG直接从体积数据集初始化，消除了对类似COLMAP的结构运动恢复需求的管道。 为了确保完整的标量场覆盖，我们引入了一种基于不透明度的训练策略，使用具有多个传输函数的可微分渲染来优化我们的数据表示。 这使得VEG能够在保持与任何特定传输函数无关的同时，保留数据集整个标量范围内的精细特征。 每个高斯被缩放和旋转以适应局部几何形状，从而在不存储网格连接的情况下高效表示非结构化网格，并且使用更少的原始图形。 在多种数据集上，VEG实现了高质量的重建，最多可压缩3600倍的大型体积数据集，并支持在商品GPU上闪电般的快速渲染，从而实现了大规模结构化和非结构化体积的交互式可视化。