ECoNGS：用于体数据可视化的高效压缩神经高斯点云
ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization
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- University of Notre Dame（圣母大学）
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中文总结 AI 辅助
研究针对大型数据集体数据可视化，提出ECoNGS框架，用轻量级神经网络从显式锚点预测隐式高斯点云，结合联合学习策略、神经熵模型压缩及特定初始化策略，相比iVR-GS在多方面性能更优。
中文摘要 AI 辅助
最近可微高斯点云的进展凸显了基于基元的方法作为大型数据集交互式、高质量体数据可视化（VolVis）的替代场景表示的潜力。然而，当前基于基元的方法的显式性质，加上对每个VolVis场景的孤立优化，导致了冗余、非紧凑的表示。我们提出了ECoNGS，一种用于VolVis场景表示的高效压缩神经高斯点云框架。ECoNGS使用轻量级神经网络从显式锚点动态预测隐式、可编辑的高斯点云，有效地将隐式表示的模型紧凑性和参数效率与显式基元的高性能渲染相结合。我们探索了一种联合学习策略，对几何相似的场景进行聚类并在它们之间共享参数，在保持重建保真度的同时显著减少了整体训练时间和模型大小。为了实现更紧凑的场景表示，我们使用估计其概率分布的神经熵模型进一步压缩显式锚点属性，通过熵编码实现紧凑存储。我们系统地研究了高斯初始化策略，并提出了一种针对VolVis场景量身定制的简单而有效的方案，提高了重建精度并加速了收敛。我们在各种单变量和多变量VolVis场景中对ECoNGS进行了定性和定量评估，突出了其在训练时间、重建质量和模型大小方面优于现有方法的性能。特别是，与现有方法iVR-GS相比，ECoNGS在PSNR中提高了高达2.2dB的重建质量，同时将模型大小减少了高达6.1倍，训练时间减少了高达5.9倍。代码可在这个https URL上获取。
英文摘要
Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations for interactive, high-quality, volume visualization (VolVis) of large datasets. However, the explicit nature of current primitive-based methods, combined with isolated optimization for each VolVis scene, results in redundant, non-compact representations. We present ECoNGS, an efficient compressive neural Gaussian splatting framework for VolVis scene representation. ECoNGS employs lightweight neural networks to dynamically predict implicit, editable Gaussian splats from explicit anchor points, effectively combining model compactness and parameter efficiency of implicit representations with high-performance rendering of explicit primitives. We explore a joint learning strategy that clusters geometrically similar scenes and shares parameters across them, significantly reducing overall training time and model size while maintaining reconstruction fidelity. To achieve a more compact scene representation, we further compress the explicit anchor attributes using a neural entropy model that estimates their probability distributions, enabling compact storage via entropy coding. We systematically investigate Gaussian initialization strategies and propose a simple yet effective scheme tailored for VolVis scenes, improving reconstruction accuracy and accelerating convergence. We evaluate ECoNGS qualitatively and quantitatively across various univariate and multivariate VolVis scenes, highlighting its superior performance over prior methods in training time, reconstruction quality, and model size. In particular, compared with the prior method iVR-GS, ECoNGS improves reconstruction quality by up to 2.2 dB in PSNR while reducing the model size by up to 6.1x and the training time by up to 5.9x. The code is available at https://github.com/TouKaienn/ECoNGS.
