# Invisible Marker: Automatic Annotation of Segmentation Masks for Object Manipulation

Source: [Invisible Marker: Automatic Annotation of Segmentation Masks for Object Manipulation](<https://www.sota2.com/research/paper/68fa68181e209131cea1a317>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:03:45.482Z

Authors: Kuniyuki Takahashi, Kenta Yonekura

Published: 2019-09-27T04:49:47.000Z

## Abstract

We propose a method to annotate segmentation masks accurately and automatically using invisible marker for object manipulation. Invisible marker is invisible under visible (regular) light conditions, but becomes visible under invisible light, such as ultraviolet (UV) light. By painting objects with the invisible marker, and by capturing images while alternately switching between regular and UV light at high speed, massive annotated datasets are created quickly and inexpensively. We show a comparison between our proposed method and manual annotations. We demonstrate semantic segmentation for deformable objects including clothes, liquids, and powders under controlled environmental light conditions. In addition, we show demonstrations of liquid pouring tasks under uncontrolled environmental light conditions in complex environments such as inside the office, house, and outdoors. Furthermore, it is possible to capture data while the camera is in motion so it becomes easier to capture large datasets, as shown in our demonstration.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.12493>)

[PDF](<https://arxiv.org/pdf/1909.12493.pdf>)