Abstract: Image vectorization is one of the primary means of creating vector graphics. The quality of a vectorized image depends crucially on extracting accurate features from input raster images. However, correct object edges can be difficult to detect when color gradients are weak. We present an image vectorization technique that operates on a color image augmented with a depth map and uses both color and depth edges to define vectorized paths. We output a vectorized result as a diffusion curve image. The information extracted from the depth map allows us more flexibility in the manipulation of the diffusion curves, in particular permitting high-level object segmentation. Our experimental results demonstrate that this method achieves high reconstruction quality and provides greater control in the organization and editing of vectorized images than existing work based on diffusion curves.
Recommended citation: Shufang Lu, Wei Jiang, Xuefeng Ding, Craig S.Kaplan, Xiaogang Jin, Fei Gao, Jiazhou Chen. “Depth-aware image vectorization and editing.” The Visual Computer. 2019, 35: 1027–1039.