体视化中的学习自适应采样与重构
Learning Adaptive Sampling and Reconstruction for Volume Visualization
摘要：
数据可视化的一个核心挑战是理解需要哪些数据样本来生成数据集的图像，其中相关信息被编码。在这项工作中，我们对回答人工神经网络是否可以预测在哪里采样的数据具有更高或更低的密度，通过学习数据，采样模式和生成的图像之间的对应关系的问题迈出了第一步。我们介绍了一种新的神经渲染管道，它是训练端到端生成一个稀疏的自适应采样结构从一个给定的低分辨率输入图像，并重建一个高分辨率的图像从稀疏的样本集。这是第一次，据我们所知，我们证明了选择的结构，是相关的最终视觉表示可以共同学习与重建的表示，从这些结构。因此，我们引入了可微分采样和重建阶段，这可以利用基于监督损失的反向传播，仅在最终图像上。我们阐明了网络管道产生的自适应采样模式，并分析了其用于体可视化，包括等值面和直接体渲染。
英文摘要：
A central challenge in data visualization is to understand which data samples are required to generate an image of a data set in which the relevant information is encoded. In this work, we make a first step towards answering the question of whether an artificial neural network can predict where to sample the data with higher or lower density, by learning of correspondences between the data, the sampling patterns and the generated images. We introduce a novel neural rendering pipeline, which is trained end-to-end to generate a sparse adaptive sampling structure from a given low-resolution input image, and reconstructs a high-resolution image from the sparse set of samples. For the first time, to the best of our knowledge, we demonstrate that the selection of structures that are relevant for the final visual representation can be jointly learned together with the reconstruction of this representation from these structures. Therefore, we introduce differentiable sampling and reconstruction stages, which can leverage back-propagation based on supervised losses solely on the final image. We shed light on the adaptive sampling patterns generated by the network pipeline and analyze its use for volume visualization including isosurface and direct volume rendering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
