Interpretation of overtracing freehand sketching for geometric shapes

This paper presents a novel method for interpreting overtracing freehand sketch. The overtracing strokes are interpreted as sketch content and are used to generate 2D geometric primitives. The approach consists of four stages: stroke classification, strokes grouping and fitting, 2D tidy-up with endpoint clustering and parallelism correction, and in-context interpretation. Strokes are first classified into lines and curves by a linearity test. It is followed by an innovative strokes grouping process that handles lines and curves separately. The grouped strokes are fitted with 2D geometry and further tidied-up with endpoint clustering and parallelism correction. Finally, the in-context interpretation is applied to detect incorrect stroke interpretation based on geometry constraints and to suggest a most plausible correction based on the overall sketch context. The interpretation ensures sketched strokes to be interpreted into meaningful output. The interface overcomes the limitation where only a single line drawing can be sketched out as in most existing sketching programs, meanwhile is more intuitive to the user

Algorithmic Perception of Vertices in Sketched Drawings of Polyhedral Shapes

In this article, visual perception principles were used to build an artificial perception model aimed at developing an algorithm for detecting junctions in line drawings of polyhedral objects that are vectorized from hand-drawn sketches. The detection is performed in two dimensions (2D), before any 3D model is available and minimal information about the shape depicted by the sketch is used. The goal of this approach is to not only detect junctions in careful sketches created by skilled engineers and designers but also detect junctions when skilled people draw casually to quickly convey rough ideas. Current approaches for extracting junctions from digital images are mostly incomplete, as they simply merge endpoints that are near each other, thus ignoring the fact that different vertices may be represented by different (but close) junctions and that the endpoints of lines that depict edges that share a common vertex may not necessarily be close to each other, particularly in quickly sketched drawings. We describe and validate a new algorithm that uses these perceptual findings to merge tips of line segments into 2D junctions that are assumed to depict 3D vertices

Tolerance Zone-Based Grouping Method for Online Multiple Overtracing Freehand Sketches

Multiple overtracing strokes are common drawing behaviors in freehand sketching; that is, additional strokes are often drawn repeatedly over the existing ones to add more details. This paper proposes a method based on stroke-tolerance zones to group multiple overtraced strokes which are drawn to express a 2D primitive, aiming to convert online freehand sketches into 2D line drawings, which is a base for further 3D reconstruction. Firstly, after the user inputs a new stroke, a tolerance zone around the stroke is constructed by reference to its polygonal approximation points obtained from the stroke preprocessing. Then, the input strokes are divided into stroke groups, each representing a primitive through the stroke grouping process based on the overtraced ratio of two strokes. At last, each stroke group is fitted into one or more 2D geometric primitives including line segments, polylines, ellipses, and arcs. The proposed method groups two strokes together based on their screen-space proximity directly instead of classifying and fitting them firstly, so that it can group strokes of arbitrary shapes. A sketch-recognition prototype system has been implemented to test the effectiveness of the proposed method. The results showed that the proposed method could support online multiple overtracing freehand sketching with no limitation on drawing sequence, but it only deals with strokes with relatively high overtraced ratio

Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches

We present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well-connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand-engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.Comment: 15 pages, 14 figure

Outline tracing from sketches.

草圖繪製是創意產業最早期的一個工序。研究發現，草圖不但表達了畫家的想法，在繪製的過程，草圖也能為畫家帶來新靈感，所以草圖繪製是不可缺少的工序。然而，原始的草圖包含許多不必要的筆觸，所以進行後期製作前，必須用線重新勾畫。傳統上，這個過程是費時和繁瑣的，畫家必須人手把每一條需要的線都重新勾畫出來。當應用到動畫創作的時候，由於涉及的草圖數量龐大，情況會變得更壊，所以有必要把整個過程自動化。現有的研究都視草圖勾勒為一個線條分組和曲線擬合的過程，他們會把相近而順暢的筆觸組合在一起，形成單一線條。然而，他們都忽略了視覺感知上的一個重要法則──格式塔理論中的閉合原理。格式塔理論是一個知名的心理學理論，解釋人類如何透過整合理解各種視覺元素。根據格式塔理論中的閉合原理，我們往往把各種分隔的視覺元素整合為一個封閉的形狀。在這篇論文中，我提出閉合原理比格式塔理論中的其他原理更能幫助我們理解草圖，從而提出了一種基於區域的方法來勾勒草圖。實驗結果發現，我的方法在保有草圖上封閉形狀的能力上比現有的方法更優勝。Sketching is the earliest stage of production in art and design. Sketches are useful in conveying and developing ideas. However, raw sketches contain unnecessary strokes and must be converted to neat and tidy drawings before moving onto later stage of production. Traditionally, this conversion process is time-consuming and tedious since it is performed stroke by stroke manually. The situation is even worse when it comes to animation production which involves a huge number of sketches, so there is a strong motivation to automate the conversion process. Existing works formulate the conversion process as stroke grouping and curve fitting processes, in which close and continuous strokes are grouped together to form single strokes in the resulting image. Nevertheless, previous works overlooked an important law of visual perception: the law of closure in Gestalt principles. Gestalt principles concluded from early visual perception studies demonstrate how human perceive visual elements as different groups of lines and shapes. It states that we tend to group elements into closed shape even when a gap exists. In this thesis, we utilize the idea of law of closure and propose a region-based approach to refine sketches. Experiment result shows that this method outperforms the existing methods in terms of the capability of preserving salient regions in sketches.Detailed summary in vernacular field only.Wong, Ka Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.Includes bibliographical references (leaves 59-61).Abstracts also in Chinese.Chapter 1 --- Introduction --- p.6Chapter 2 --- Background --- p.8Chapter 2.1 --- Role of Sketch in Art and Design --- p.8Chapter 2.2 --- Characteristics of Raw Sketches --- p.10Chapter 2.3 --- Related Works --- p.11Chapter 2.3.1 --- Methods for Raster Image --- p.12Chapter 2.3.2 --- Methods for Vector Image --- p.14Chapter 2.3.3 --- Deficiency --- p.17Chapter 3 --- Gestalt Principles and Its Application --- p.18Chapter 3.1 --- Introduction to Gestalt Principles --- p.18Chapter 3.2 --- Existing Computational Model of Gestalt Principles --- p.20Chapter 3.3 --- Gestalt Principles for Outline Tracing --- p.22Chapter 3.3.1 --- Similarity --- p.23Chapter 3.3.2 --- Proximity --- p.23Chapter 3.3.3 --- Continuity --- p.24Chapter 3.3.4 --- Regularity --- p.25Chapter 3.3.5 --- Closure --- p.25Chapter 4 --- Proposed Method --- p.27Chapter 4.1 --- Multi-scale Region Retrieval --- p.28Chapter 4.1.1 --- Construction of Region Hierarchy --- p.29Chapter 4.1.2 --- Region Refinement --- p.31Chapter 4.2 --- Salient Region Retrieval --- p.32Chapter 4.2.1 --- Flattening of Region Hierarchy --- p.32Chapter 4.2.2 --- Region Merging --- p.33Chapter 4.2.3 --- Region Pruning --- p.37Chapter 4.2.4 --- Region Merging by User --- p.41Chapter 4.3 --- Outline Synthesis --- p.43Chapter 4.3.1 --- Outline Synthesis of Region-boundary Strokes --- p.44Chapter 4.3.2 --- Smoothing of Region-boundary Strokes --- p.45Chapter 4.3.3 --- Outline Synthesis of Feature Strokes --- p.48Chapter 4.4 --- Curve Fitting --- p.50Chapter 5 --- Result and Discussion --- p.52Chapter 6 --- Conclusion --- p.58Chapter 7 --- Reference --- p.5

SculptFlow: Visualizing Sculpting Sequences by Continuous Summarization

Digital sculpting is becoming ubiquitous for modeling organic shapes like characters. Artists commonly show their sculpting sessions by producing timelapses or speedup videos. But the long length of these sessions make these visualizations either too long to remain interesting or too fast to be useful. In this paper, we present SculptFlow, an algorithm that summarizes sculpted mesh sequences by repeatedly merging pairs of subsequent edits taking into account the number of summarized strokes, the magnitude of the edits, and whether they overlap. Summaries of any length are generated by stopping the merging process when the desired length is reached. We enhance the summaries by highlighting edited regions and drawing filtered strokes to indicate artists\u27 workflows. We tested SculptFlow by recording professional artists as they modeled a variety of meshes, from detailed heads to full bodies. When compared to speedup videos, we believe that SculptFlow produces more succinct and informative visualizations. We open source SculptFlow for artists to show their work and release all our datasets so that others can improve upon our work

Sketch recognition of digital ink diagrams : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Figures are either re-used with permission, or abstracted with permission from the source article.Sketch recognition of digital ink diagrams is the process of automatically identifying hand-drawn elements in a diagram. This research focuses on the simultaneous grouping and recognition of shapes in digital ink diagrams. In order to recognise a shape, we need to group strokes belonging to a shape, however, strokes cannot be grouped until the shape is identified. Therefore, we treat grouping and recognition as a simultaneous task. Our grouping technique uses spatial proximity to hypothesise shape candidates. Many of the hypothesised shape candidates are invalid, therefore we need a way to reject them. We present a novel rejection technique based on novelty detection. The rejection method uses proximity measures to validate a shape candidate. In addition, we investigate on improving the accuracy of the current shape recogniser by adding extra features. We also present a novel connector recognition system that localises connector heads around recognised shapes. We perform a full comparative study on two datasets. The results show that our approach is significantly more accurate in finding shapes and faster on process diagram compared to Stahovich et al. (2014), which the results show the superiority of our approach in terms of computation time and accuracy. Furthermore, we evaluate our system on two public datasets and compare our results with other approaches reported in the literature that have used these dataset. The results show that our approach is more accurate in finding and recognising the shapes in the FC dataset (by finding and recognising 91.7% of the shapes) compared to the reported results in the literature

Fidelity vs. Simplicity: a Global Approach to Line Drawing Vectorization

International audienceVector drawing is a popular representation in graphic design because of the precision, compactness and editability offered by parametric curves. However, prior work on line drawing vectorization focused solely on faithfully capturing input bitmaps, and largely overlooked the problem of producing a compact and editable curve network. As a result, existing algorithms tend to produce overly-complex drawings composed of many short curves and control points, especially in the presence of thick or sketchy lines that yield spurious curves at junctions. We propose the first vectorization algorithm that explicitly balances fidelity to the input bitmap with simplicity of the output, as measured by the number of curves and their degree. By casting this trade-off as a global optimization, our algorithm generates few yet accurate curves, and also disambiguates curve topology at junctions by favoring the simplest interpretations overall. We demonstrate the robustness of our algorithm on a variety of drawings, sketchy cartoons and rough design sketches

3DFlow: Continuous Summarization of Mesh Editing Workflows

Mesh editing software is continually improving allowing more detailed meshes to be create efficiently by skilled artists. Many of these are interested in sharing not only the final mesh, but also their whole workflows both for creating tutorials as well as for showcasing the artist\u27s talent, style, and expertise. Unfortunately, while creating meshes is improving quickly, sharing editing workflows remains cumbersome since time-lapsed or sped-up videos remain the most common medium. In this paper, we present 3DFlow, an algorithm that computes continuous summarizations of mesh editing workflows. 3DFlow takes as input a sequence of meshes and outputs a visualization of the workflow summarized at any level of detail. The output is enhanced by highlighting edited regions and, if provided, overlaying visual annotations to indicated the artist\u27s work, e.g. summarizing brush strokes in sculpting. We tested 3DFlow with a large set of inputs using a variety of mesh editing techniques, from digital sculpting to low-poly modeling, and found 3DFlow performed well for all. Furthermore, 3DFlow is independent of the modeling software used since it requires only mesh snapshots, using additional information only for optional overlays. We open source 3DFlow for artists to showcase their work and release all our datasets so other researchers can improve upon our work