mlsanityData to 7 Oct 2026

Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds

arXiv 2512.07211 ↗First posted 8 Dec 2025cs.CVcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 8 Dec 2025.

Builds on

37 of 47 references.

References · 37

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25 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Towards a Modular Bin-picking Framework for Handling Object Pose Uncertaintiescited by 0