GaussianPile：面向切片式体数据重建的统一稀疏高斯泼溅框架
GaussianPile: A Unified Sparse Gaussian Splatting Framework for Slice-based Volumetric Reconstruction
将各向异性 3D 高斯与成像系统点扩散函数结合，用于切片式体数据的压缩与重建，其可微投影算子和紧凑编码管线可迁移到其他需要物理感知渲染的 3D 重建任务。
原文依据 a differentiable projection operator that encodes the finite-thickness point spread function of the imaging acquisition system
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于切片的体成像应用广泛，它要求表示方法在积极压缩的同时保留内部结构以供分析。本文提出 GaussianPile，将 3D 高斯泼溅与成像系统感知的聚焦模型统一起来以应对这一挑战。我们的新方法引入三项关键创新：(i) 切片感知的堆叠策略，通过定位各向异性 3D 高斯来建模跨切片贡献；(ii) 可微投影算子，编码成像采集系统的有限厚度点扩散函数；(iii) 紧凑编码与联合优化流程，同时重建和压缩高斯集合。我们基于 CUDA 的设计保留了高斯基元的压缩和实时渲染效率，同时保持高频内部体细节。在显微和超声数据集上的实验表明，我们的方法降低了存储和重建成本，保持了诊断保真度，并支持快速 2D 可视化以及 3D 体素化。实践中，它能在短短 3 分钟内产生高质量结果——比基于 NeRF 的方法快达 11 倍——并实现相对于原始体素网格一致的 16 倍压缩，为可部署的压缩和切片式体数据集的探索提供了实用路径。
英文原文摘要 1377 字符
Slice-based volumetric imaging is widely applied and it demands representations that compress aggressively while preserving internal structure for analysis. This paper introduces GaussianPile, unifying 3D Gaussian splatting with an imaging system-aware focus model to address this challenge. Our new method introduces three key innovations: (i) a slice-aware piling strategy that positions anisotropic 3D Gaussians to model through-slice contributions, (ii) a differentiable projection operator that encodes the finite-thickness point spread function of the imaging acquisition system, and (iii) a compact encoding and joint optimization pipeline that simultaneously reconstructs and compresses the Gaussian sets. Our CUDA-based design retains the compression and real-time rendering efficiency of Gaussian primitives while preserving high-frequency internal volumetric detail. Experiments on microscopy and ultrasound datasets demonstrate that our method reduces storage and reconstruction cost, sustains diagnostic fidelity, and enables fast 2D visualization, along with 3D voxelization. In practice, it delivers high-quality results in as few as 3 minutes--up to 11xfaster than NeRF-based approaches--and achieves consistent 16xcompression over the original voxel grids, offering a practical path to deployable compression and exploration of slice-based volumetric datasets.