通过隐式表面提取实现深度草图矢量化

IF 4.4 2区 化学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Chuan Yan, Yong Li, Deepali Aneja, M. Fisher, Edgar Simo-Serra, Y. Gingold
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引用次数: 0

摘要

我们介绍了一种草图矢量化算法,该算法具有一流的精确度,能够处理复杂的草图。我们将草图矢量化作为一项从无符号距离场中提取曲面的任务,并采用两阶段神经网络和双轮廓域后处理算法来实现。第一阶段包括从输入光栅图像中提取无符号距离场。第二阶段包括一个改进的神经双轮廓网络,该网络对噪声输入具有更强的鲁棒性,对线条几何形状也更加敏感。为了解决基于网格的曲面提取方法固有的采样不足问题,我们明确预测了采样不足和关键点图。我们的后处理算法会使用这些地图来解决尖锐特征和多向交界的问题。关键点和欠采样图是自然可控的,我们在交互式拓扑细化界面中演示了这一点。我们提出的方法在复杂输入上产生的矢量化比以前的方法精确得多,而且运行时间短。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Sketch Vectorization via Implicit Surface Extraction
We introduce an algorithm for sketch vectorization with state-of-the-art accuracy and capable of handling complex sketches. We approach sketch vectorization as a surface extraction task from an unsigned distance field, which is implemented using a two-stage neural network and a dual contouring domain post processing algorithm. The first stage consists of extracting unsigned distance fields from an input raster image. The second stage consists of an improved neural dual contouring network more robust to noisy input and more sensitive to line geometry. To address the issue of under-sampling inherent in grid-based surface extraction approaches, we explicitly predict undersampling and keypoint maps. These are used in our post-processing algorithm to resolve sharp features and multi-way junctions. The keypoint and undersampling maps are naturally controllable, which we demonstrate in an interactive topology refinement interface. Our proposed approach produces far more accurate vectorizations on complex input than previous approaches with efficient running time.
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来源期刊
CiteScore
7.20
自引率
6.00%
发文量
810
期刊介绍: ACS Applied Polymer Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics, and biology relevant to applications of polymers. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates fundamental knowledge in the areas of materials, engineering, physics, bioscience, polymer science and chemistry into important polymer applications. The journal is specifically interested in work that addresses relationships among structure, processing, morphology, chemistry, properties, and function as well as work that provide insights into mechanisms critical to the performance of the polymer for applications.
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