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iVR-GS:通过可编辑的3D高斯点渲染实现可探索可视化的逆体积渲染

iVR-GS: Inverse Volume Rendering for Explorable Visualization via Editable 3D Gaussian Splatting.

作者信息

Tang Kaiyuan, Yao Siyuan, Wang Chaoli

出版信息

IEEE Trans Vis Comput Graph. 2025 Jun;31(6):3783-3795. doi: 10.1109/TVCG.2025.3567121.

Abstract

In volume visualization, users can interactively explore the three-dimensional data by specifying color and opacity mappings in the transfer function (TF) or adjusting lighting parameters, facilitating meaningful interpretation of the underlying structure. However, rendering large-scale volumes demands powerful GPUs and high-speed memory access for real-time performance. While existing novel view synthesis (NVS) methods offer faster rendering speeds with lower hardware requirements, the visible parts of a reconstructed scene are fixed and constrained by preset TF settings, significantly limiting user exploration. This article introduces inverse volume rendering via Gaussian splatting (iVR-GS), an innovative NVS method that reduces the rendering cost while enabling scene editing for interactive volume exploration. Specifically, we compose multiple iVR-GS models associated with basic TFs covering disjoint visible parts to make the entire volumetric scene visible. Each basic model contains a collection of 3D editable Gaussians, where each Gaussian is a 3D spatial point that supports real-time scene rendering and editing. We demonstrate the superior reconstruction quality and composability of iVR-GS against other NVS solutions (Plenoxels, CCNeRF, and base 3DGS) on various volume datasets. The code is available at https://github.com/TouKaienn/iVR-GS.

摘要

在体可视化中,用户可以通过在传递函数(TF)中指定颜色和不透明度映射或调整光照参数来交互式地探索三维数据,从而便于对底层结构进行有意义的解释。然而,渲染大规模体数据需要强大的图形处理器(GPU)和高速内存访问以实现实时性能。虽然现有的新颖视图合成(NVS)方法以较低的硬件要求提供了更快的渲染速度,但重建场景的可见部分是固定的,并受预设TF设置的限制,这极大地限制了用户的探索。本文介绍了基于高斯点云的逆体渲染(iVR-GS),这是一种创新的NVS方法,它在降低渲染成本的同时,还能实现场景编辑以进行交互式体探索。具体来说,我们组合了多个与覆盖不相交可见部分的基本TF相关联的iVR-GS模型,以使整个体场景可见。每个基本模型都包含一组3D可编辑高斯点云,其中每个高斯点云都是一个支持实时场景渲染和编辑的3D空间点。我们在各种体数据集上展示了iVR-GS相对于其他NVS解决方案(Plenoxels、CCNeRF和基础3DGS)的卓越重建质量和可组合性。代码可在https://github.com/TouKaienn/iVR-GS获取。

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