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

arXiv: https://arxiv.org/abs/2411.13149 · HTML: https://mlsanity.com/paper/2411.13149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects](https://mlsanity.com/paper/2403.09799/) (2024)
- [YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation](https://mlsanity.com/paper/2004.11657/) (2020)
- [Extending 6D Object Pose Estimators for Stereo Vision](https://mlsanity.com/paper/2402.05610/) (2024)
- [Fast Training Data Acquisition for Object Detection and Segmentation using Black Screen Luminance Keying](https://mlsanity.com/paper/2405.07653/) (2024)
- [YCB-Ev 1.1: Event-vision dataset for 6DoF object pose estimation](https://mlsanity.com/paper/2309.08482/) (2023)

## Abstract (arXiv)

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.