Summary of Uncertainty Quantification with Deep Ensembles For 6d Object Pose Estimation, by Kira Wursthorn et al.
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation
by Kira Wursthorn, Markus Hillemann, Markus Ulrich
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach to estimating 6D object poses is introduced, which combines multiple stages using deep learning. The method, SurfEmb, is based on existing top-performing approaches and allows for end-to-end training with deep ensembles for uncertainty quantification. This enables the estimation of well-calibrated and robust uncertainty estimates in high-risk scenarios like human-robot interaction, industrial inspection, and automation. Evaluation metrics and concepts are applied to assess the results, including a novel uncertainty calibration score for regression tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about estimating where objects are in 3D space. It’s really important when robots or machines need to work with people or things precisely. The current best methods use deep learning, but they have some limitations. This new method makes it possible to estimate not only the object’s location but also how certain we can be of that estimation. This is helpful in situations where small mistakes could cause big problems. |
Keywords
» Artificial intelligence » Deep learning » Regression
