GaussianPile：一种统一的稀疏高斯散射框架用于基于切片的体积分离重建
GaussianPile: A Unified Sparse Gaussian Splatting Framework for Slice-based Volumetric Reconstruction
AI总结：
本文提出GaussianPile框架，结合3D高斯散射与成像系统感知的聚焦模型，通过切片感知堆叠策略、可微投影算子和紧凑编码优化流程，实现高效体积分离重建，提升压缩效率与诊断精度。
摘要：
基于切片的体积分离成像广泛应用于需要高效压缩同时保留内部结构的分析场景。我们引入GaussianPile，将3D高斯散射与成像系统感知的聚焦模型统一，以解决这一挑战。我们的方法引入三个关键创新：(i) 一种切片感知的堆叠策略，用于定位各向异性3D高斯以建模切片内的贡献；(ii) 一种可微的投影算子，编码成像采集系统的有限厚度点扩散函数；(iii) 一种紧凑编码和联合优化流程，同时重建和压缩高斯集。我们的基于CUDA的设计在保持高斯原始的压缩和实时渲染效率的同时，保留了高频率的内部体积分离细节。在显微镜和超声数据集上的实验表明，我们的方法减少了存储和重建成本，保持了诊断精度，并实现了快速的2D可视化，以及3D体素化。在实践中，它能在3分钟内交付高质量结果，比NeRF方法快11倍，并在体素网格上实现16倍的压缩，提供了一条部署压缩和探索基于切片的体积分离数据集的实用路径。
英文摘要：
Slice-based volumetric imaging is widely applied and it demands representations that compress aggressively while preserving internal structure for analysis. We introduce GaussianPile, unifying 3D Gaussian splatting with an imaging system-aware focus model to address this challenge. Our proposed 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 11x faster than NeRF-based approaches, and achieves consistent 16x compression over voxel grids, offering a practical path to deployable compression and exploration of slice-based volumetric datasets.
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