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. This word is normally spelled with a hyphen. 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.

@Article{ChuanSIGGRAPH2024,
   author    = {Chuan Yan and Yong Li and Deepali Aneja and Matthew Fisher and Edgar Simo-Serra and Yotam Gingold},
   title     = {{Deep Sketch Vectorization via Implicit Surface Extraction}},
   journal   = "ACM Transactions on Graphics (SIGGRAPH)",
   year      = 2024,
   volume    = 43,
   number    = 4,
}

We present an interactive approach for inking, which is the process of turning a pencil rough sketch into a clean line drawing. The approach, which we call the Smart Inker, consists of several "smart" tools that intuitively react to user input, while guided by the input rough sketch, to efficiently and naturally connect lines, erase shading, and fine-tune the line drawing output. Our approach is data-driven: the tools are based on fully convolutional networks, which we train to exploit both the user edits and inaccurate rough sketch to produce accurate line drawings, allowing high-performance interactive editing in real-time on a variety of challenging rough sketch images. For the training of the tools, we developed two key techniques: one is the creation of training data by simulation of vague and quick user edits; the other is a line normalization based on learning from vector data. These techniques, in combination with our sketch-specific data augmentation, allow us to train the tools on heterogeneous data without actual user interaction. We validate our approach with an in-depth user study, comparing it with professional illustration software, and show that our approach is able to reduce inking time by a factor of 1.8x while improving the results of amateur users.

@Article{SimoSerraSIGGRAPH2018,
   author    = {Edgar Simo-Serra and Satoshi Iizuka and Hiroshi Ishikawa},
   title     = {{Real-Time Data-Driven Interactive Rough Sketch Inking}},
   journal   = "ACM Transactions on Graphics (SIGGRAPH)",
   year      = 2018,
   volume    = 37,
   number    = 4,
}