通过分析权重矩阵，用剪枝法实现隐式神经表示的高效自适应网格可视化。
Pruning AMR: Efficient Visualization of Implicit Neural Representations via Weight Matrix Analysis
- 基于权重矩阵的插值分解剪枝，识别隐式神经表示中的几何特征。
- 无需原始数据，从预训练INR生成可变分辨率网格，内存节省显著。
- 适合需要高效4D医学成像可视化的研究人员使用。
隐式神经表示（INR）是一种逼近时空函数的神经网络。许多高内存消耗的可视化任务，如现代4D CT扫描方法，原生以INR形式表示数据。尽管INR相比传统网格存储更省内存，但多数可视化仍需离散化为规则网格。本文提出PruningAMR算法，利用INR权重矩阵的插值分解剪枝方法识别其编码的几何特征，并据此构建自适应分辨率的网格。该剪枝后的网络用于引导自适应网格细化，实现针对函数底层分辨率的自动网格生成。在不访问训练数据的前提下，仅凭预训练的INR即可生成高内存效率的可变分辨率可视化结果。
原文摘要 · Abstract (English)
An implicit neural representation (INR) is a neural network that approximates a spatiotemporal function. Many memory-intensive visualization tasks, including modern 4D CT scanning methods, represent data natively as INRs. While INRs are prized for being more memory-efficient than traditional data stored on a lattice, many visualization tasks still require discretization to a regular grid. We present PruningAMR, an algorithm that builds a mesh with resolution adapted to geometric features encoded by the INR. To identify these geometric features, we use an interpolative decomposition pruning method on the weight matrices of the INR. The resulting pruned network is used to guide adaptive mesh refinement, enabling automatic mesh generation tailored to the underlying resolution of the function. Starting from a pre-trained INR--without access to its training data--we produce a variable resolution visualization with substantial memory savings.
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