CAMION: Cascade Multi-Input Multi-Output Network for Skeleton Extraction

Abstract

Skeletonization is an important process of extracting the medial axis of the object shape while maintaining the original geometric and topological properties. Some recent studies have demonstrated that deep learning-based segmentation models can extract the main skeleton from objects more robustly. However, we find that the skeleton extracted by a vanilla segmentation process is always discontinuous and not accurate enough. In this paper, we propose a general cascade deep learning pipeline that achieves competitive performance only using a simple U-shape network. The semantic information contained in the shapes is limited, so we introduce a ConvNet with multi-source input and multi-task output, CAMION for short, on top of the basic shape-to-skeleton network. With the multi-source inputs, CAMION can converge faster than using only bi-nary shapes; and with the introduction of multi-task learning, relevant and suitable auxiliary tasks (e.g., feature point detection and contour extraction) bring considerable gains for the extraction of skeleton. Our code used in Pixel Skel-NetOn - CVPR 2022 challenge will be released at https://github.com/likyoo/CAMION-CVPRW2022.