AI 中文总结
针对现有3DGS方法依赖DVR图像学习导致信息损失与传递函数控制受限的问题，提出ESVR框架，结合结构感知基元学习与逐基元光线采样等技术，实现大规模稀疏体数据的高效压缩与实时高质量渲染。
AI 中文摘要
在科学可视化领域，大规模稀疏体数据的高效表示与绘制仍具挑战性，因为有意义的结构通常仅占空间域的一小部分。直接体绘制（DVR）可提供高质量可视化，但其计算和内存成本随数据规模增长的扩展性较差。近期3D高斯溅射（3DGS）的进展通过用紧凑几何基元表示体场景，实现了高效、高保真的绘制，不过现有基于3DGS的方法是从DVR渲染图像而非原始体数据中学习，导致信息损失，并限制了交互式探索所需的灵活传递函数控制。为解决这些局限，我们提出ESVR，一种基于椭球的稀疏体绘制框架，可直接在三维空间中学习并渲染体数据。该方法结合了具有有界支撑的可微椭球基元、带互补剪枝的结构感知基元学习，以及用于快速准确传递函数映射的逐基元光线采样策略。为支持大规模数据集，我们进一步引入了带幽灵椭球的分块优化方案，在训练期间提供边界上下文。在大型稀疏数据集上，ESVR实现了最高达四个数量级的压缩，且在保持竞争力的重建质量的同时，以43至223 FPS的帧率实现实时渲染。
英文摘要
Efficient representation and rendering of large-scale sparse volumetric data remain challenging in scientific visualization, as meaningful structures often occupy only a small fraction of the spatial domain. While direct volume rendering (DVR) provides high-quality visualization, its computational and memory costs scale poorly with data size. Recent advances in 3D Gaussian Splatting (3DGS) address this challenge by representing volumetric scenes with compact geometric primitives, enabling efficient, high-fidelity rendering. However, existing 3DGS-based methods learn from DVR rendered images rather than raw volumes, leading to information loss and limiting flexible transfer function control for interactive exploration. To address these limitations, we propose ESVR, an ellipsoid-based sparse volume rendering framework that directly learns and renders volumetric data in 3D space. Our method combines differentiable ellipsoidal primitives with bounded support, structure-aware primitive learning with complementary pruning, and a per-primitive ray sampling strategy for fast and accurate transfer function mapping. To support large-scale datasets, we further introduce a chunk-based optimization scheme with ghost ellipsoids, providing boundary context during training. Across large sparse datasets, ESVR achieves up to four orders of magnitude compression and real-time rendering at 43-223 FPS while maintaining competitive reconstruction quality.
CommentsIEEE VIS 2026 accepted
