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YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization

Thomas Pöllabauer

TL;DR

The additional variety of objects - addition of transparency, multiple color variations, non-rigid objects - further demonstrates the usefulness of luminance keying and might be used to test the applicability of the approach on new 2D object detection and segmentation algorithms.

Abstract

Localizing target objects in images is an important task in computer vision. Often it is the first step towards solving a variety of applications in autonomous driving, maintenance, quality insurance, robotics, and augmented reality. Best in class solutions for this task rely on deep neural networks, which require a set of representative training data for best performance. Creating sets of sufficient quality, variety, and size is often difficult, error prone, and expensive. This is where the method of luminance keying can help: it provides a simple yet effective solution to record high quality data for training object detection and segmentation. We extend previous work that presented luminance keying on the common YCB-V set of household objects by recording the remaining objects of the YCB superset. The additional variety of objects - addition of transparency, multiple color variations, non-rigid objects - further demonstrates the usefulness of luminance keying and might be used to test the applicability of the approach on new 2D object detection and segmentation algorithms.

YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization

TL;DR

The additional variety of objects - addition of transparency, multiple color variations, non-rigid objects - further demonstrates the usefulness of luminance keying and might be used to test the applicability of the approach on new 2D object detection and segmentation algorithms.

Abstract

Localizing target objects in images is an important task in computer vision. Often it is the first step towards solving a variety of applications in autonomous driving, maintenance, quality insurance, robotics, and augmented reality. Best in class solutions for this task rely on deep neural networks, which require a set of representative training data for best performance. Creating sets of sufficient quality, variety, and size is often difficult, error prone, and expensive. This is where the method of luminance keying can help: it provides a simple yet effective solution to record high quality data for training object detection and segmentation. We extend previous work that presented luminance keying on the common YCB-V set of household objects by recording the remaining objects of the YCB superset. The additional variety of objects - addition of transparency, multiple color variations, non-rigid objects - further demonstrates the usefulness of luminance keying and might be used to test the applicability of the approach on new 2D object detection and segmentation algorithms.

Paper Structure

This paper contains 4 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Discussion

Figures (2)

  • Figure 1: Additional objects to complement the YCB-V set. For each object, we took multiple recordings to capture it from all sides. For deformable objects, such as the yellow chain, we record multiple different states of deformation.
  • Figure 2: Description of our recordings. Each object is attributed to a category and given a name according to the original publication where possible. We also report whether the object is part of this recording session or not (the YCB-V objects have been recorded previously), whether the recorded objects differ in some way compared to the original YCB data, and some additional meaningful meta data.