Invisible Marker: Automatic Annotation of Segmentation Masks for Object Manipulation

Abstract

We propose invisible marker for accurate automatic annotation to manipulate objects. Invisible marker is invisible in visible light, whereas it can be visible by applying ultraviolet light in the dark. By capturing images while alternately switching between visible and invisible light at high speed, massive annotation datasets for objects painted with invisible marker are created quickly and inexpensively. We show comparison with manual annotation and demonstrations of semantic segmentation by deep learning for deformable objects such as cloth, liquid, and powder. 1 * The starred authors are contributed equally.† K. Takahashi and K. Yonekura are associated with Preferred Networks, Inc.