Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914186267721728 |
|---|---|
| author | Hagelskjær, Frederik Arapis, Dimitrios Madsen, Steffen Iversen, Thorbjørn Mosekjær |
| author_facet | Hagelskjær, Frederik Arapis, Dimitrios Madsen, Steffen Iversen, Thorbjørn Mosekjær |
| contents | Object pose estimation is crucial to robotic perception and typically provides a single-pose estimate. However, a single estimate cannot capture pose uncertainty deriving from visual ambiguity, which can lead to unreliable behavior. Existing pose distribution methods rely heavily on color information, often unavailable in industrial settings.
We propose a novel neural network-based method for estimating object pose uncertainty using only 3D colorless data. To the best of our knowledge, this is the first approach that leverages deep learning for pose distribution estimation without relying on RGB input. We validate our method in a real-world bin picking scenario with objects of varying geometric ambiguity. Our current implementation focuses on symmetries in reflection and revolution, but the framework is extendable to full SE(3) pose distribution estimation. Source code available at opde3d.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds Hagelskjær, Frederik Arapis, Dimitrios Madsen, Steffen Iversen, Thorbjørn Mosekjær Computer Vision and Pattern Recognition Object pose estimation is crucial to robotic perception and typically provides a single-pose estimate. However, a single estimate cannot capture pose uncertainty deriving from visual ambiguity, which can lead to unreliable behavior. Existing pose distribution methods rely heavily on color information, often unavailable in industrial settings. We propose a novel neural network-based method for estimating object pose uncertainty using only 3D colorless data. To the best of our knowledge, this is the first approach that leverages deep learning for pose distribution estimation without relying on RGB input. We validate our method in a real-world bin picking scenario with objects of varying geometric ambiguity. Our current implementation focuses on symmetries in reflection and revolution, but the framework is extendable to full SE(3) pose distribution estimation. Source code available at opde3d.github.io |
| title | Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.07211 |