DeepIcon能从位图直接生成可编辑的矢量图标，效果更准且无冗余路径。
DeepIcon: A Hierarchical Network for Layer-wise Icon Vectorization
- 分层网络逐层生成矢量路径，理解图像语义结构。
- 无需可微分光栅化器，直接输出SVG，效率更高。
- 适合需要高质量可编辑图标的设计人员使用。
与成熟的光栅化技术不同，图像矢量化在计算机图形学中仍具挑战性。近年来基于学习的方法在将位图转换为矢量格式时，常出现形状不完整、路径冗余和语义保真度不足等问题，严重影响后续编辑与操作。为此，我们提出DeepIcon——一种专为生成变长图标矢量图形设计的分层图像矢量化网络。实验表明，DeepIcon能直接从位图高效生成可缩放矢量图形（SVG），无需可微分光栅化器，并展现出对图像内容的深刻理解。
原文摘要 · Abstract (English)
In contrast to the well-established technique of rasterization, vectorization of images poses a significant challenge in the field of computer graphics. Recent learning-based methods for converting raster images to vector formats frequently suffer from incomplete shapes, redundant path prediction, and a lack of accuracy in preserving the semantics of the original content. These shortcomings severely hinder the utility of these methods for further editing and manipulation of images. To address these challenges, we present DeepIcon, a novel hierarchical image vectorization network specifically tailored for generating variable-length icon vector graphics based on the raster image input. Our experimental results indicate that DeepIcon can efficiently produce Scalable Vector Graphics (SVGs) directly from raster images, bypassing the need for a differentiable rasterizer while also demonstrating a profound understanding of the image contents.
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