Keypoint-driven line drawing vectorization via PolyVector flow

Puhachov, Ivan; Neveu, William; Chien, Edward; Bessmeltsev, Mikhail

doi:10.1145/3478513.3480529

Cited by 10 publications

(33 citation statements)

References 74 publications

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“…Prior works can also be classified into non‐learning‐based and learning‐based approaches. The former generally perform stroke decomposition to the input line drawings followed by curve approximation [NHS*13; FLB16; LRS18; BS19; SBBB20], while the latter relies on a pre‐trained model to infer curve segmentation or/and representation [SII18a; SII18b; GZH*19; EVA*20; DYH*21; PNCB21]. Our work also requires boundary curve decomposition and approximation, and outputs a vectorized shape.…”

Section: Related Workmentioning

confidence: 99%

Img2Logo: Generating Golden Ratio Logos from Images

Hsiao

Yang

Chiu

et al. 2023

Computer Graphics Forum

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Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden‐ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error‐prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc.

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Section: Related Workmentioning

confidence: 99%

Img2Logo: Generating Golden Ratio Logos from Images

Hsiao

Yang

Chiu

et al. 2023

Computer Graphics Forum

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“…Generally, the building vector shape extraction belongs to the field of image vector extraction in computer vision. The image vectorization methods can be roughly divided into four categories: Hough-transformation-based method [12], thinningbased method [13], contour-based method [14], and cornerbased method [15], [16]. In this article, the geometric features of buildings are more obvious in VHR remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

A Building Shape Vectorization Hierarchy From VHR Remote Sensing Imagery Combined DCNNs-Based Edge Detection and PCA-Based Corner Extraction

Wen

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The automatic vectorization of building shape from very high resolution remote sensing imagery is fundamental in many fields, such as urban management and geodatabase updating. Recently, deep convolutional neural networks (DCNNs) have been successfully used for building edge detection, but the results are raster images rather than vectorized maps and do not meet the requirements of many applications. Although there are some algorithms for converting raster images into vector maps, such vector maps often have too many vector points and irregular shapes. This article proposed a building shape vectorization hierarchy, which combined DCNNs-based building edge detection and a corner extraction algorithm based on principle component analysis for rapidly extracting building corners from the building edges. Experiments on the Jiangbei New Area Buildings and Massachusetts Buildings datasets showed that compared with the state-of-the-art corner detectors, the building vector corners extracted using our proposed algorithm had fewer breakpoints and isolated points, and our building vector boundaries were more complete and regular. In addition, the building shapes extracted using our hierarchy were 7.94% higher than the nonmaximum suppression method in terms of relaxed overall accuracy on the Massachusetts dataset. Overall, our proposed hierarchy is effective for building shape vectorization.

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“…1b). This helps disambiguate directions around junctions more robustly [BS19,PNCB21,SBBB20]. However, the quality of the final vectorization depends heavily on the quality of the underlying frame field.…”

Section: Introductionmentioning

confidence: 99%

“…1d). Precisely, singularities can lead to inconsistent topology and geometry, such as gaps or spurious connections, for both tracing‐based [BS19,PNCB21] and parameterization‐based vectorization methods [SBBB20].…”

Section: Introductionmentioning

confidence: 99%

Singularity‐Free Frame Fields for Line Drawing Vectorization

Guţan,

Hegde,

Berumen

et al. 2023

Computer Graphics Forum

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State‐of‐the‐art methods for line drawing vectorization rely on generated frame fields for robust direction disambiguation, with each of the two axes aligning to different intersecting curve tangents around junctions. However, a common source of topological error for such methods are frame field singularities. To remedy this, we introduce the first frame field optimization framework guaranteed to produce singularity‐free fields aligned to a line drawing. We first perform a convex solve for a roughly‐aligned orthogonal frame field (cross field), and then comb away its internal singularities with an optimal transport–based matching. The resulting topology of the field is strictly maintained with the machinery of discrete trivial connections in a final, non‐convex optimization that allows non‐orthogonality of the field, improving smoothness and tangent alignment. Our frame fields can serve as a drop‐in replacement for frame field optimizations used in previous work, improving the quality of the final vectorizations.

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Keypoint-driven line drawing vectorization via PolyVector flow

Cited by 10 publications

References 74 publications

Img2Logo: Generating Golden Ratio Logos from Images

Img2Logo: Generating Golden Ratio Logos from Images

A Building Shape Vectorization Hierarchy From VHR Remote Sensing Imagery Combined DCNNs-Based Edge Detection and PCA-Based Corner Extraction

Singularity‐Free Frame Fields for Line Drawing Vectorization

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