  Traditional sketch segmentation methods mainly rely on handcrafted features
and complicate models, and their performance is far from satisfactory due to
the abstract representation of sketches. Recent success of Deep Neural Networks
(DNNs) in related tasks suggests DNNs could be a practical solution for this
problem, yet the suitable datasets for learning and evaluating DNNs are
limited. To this end, we introduce SketchSeg, a large dataset consisting of
10,000 pixel-wisely labeled sketches.Besides, due to the lack of colors and
textures in sketches, conventional DNNs learned on natural images are not
optimal for tackling our problem.Therefore, we further propose the Multi-column
Point-CNN (MCPNet), which (1) directly takes sampled points as its input to
reduce computational costs, and (2) adopts multiple columns with different
filter sizes to better capture the structures of sketches. Extensive
experiments validate that the MCPNet is superior to conventional DNNs like FCN.
The SketchSeg dataset is publicly available on
https://drive.google.com/open?id=1OpCBvkInhxvfAHuVs-spDEppb8iXFC3C.
