RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation

MCML Authors

Junwen Huang

→ Group Nassir Navab
 Computer Aided Medical Procedures & Augmented Reality

Nassir Navab

Prof. Dr.

Principal Investigator

Computer Aided Medical Procedures & Augmented Reality

Benjamin Busam

Prof. Dr.

Principal Investigator

Photogrammetry and Remote Sensing

Abstract

Typical template-based object pose pipelines estimate the pose by retrieving the closest matching template and aligning it with the observed image. However, failure to retrieve the correct template often leads to inaccurate pose predictions. To address this, we reformulate template-based object pose estimation as a ray alignment problem, where the viewing directions from multiple posed template images are learned to align with a non-posed query image. Inspired by recent progress in diffusion-based camera pose estimation, we embed this formulation into a diffusion transformer architecture that aligns a query image with a set of posed templates. We reparameterize object rotation using object-centered camera rays and model object translation by extending scale-invariant translation estimation to dense translation offsets. Our model leverages geometric priors from the templates to guide accurate query pose inference. A coarse-to-fine training strategy based on narrowed template sampling improves performance without modifying the network architecture. Extensive experiments across multiple benchmark datasets show competitive results of our method compared to state-of-the-art approaches in unseen object pose estimation.

inproceedings

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ICCV 2025

IEEE/CVF International Conference on Computer Vision. Honolulu, Hawai'i, Oct 19-23, 2025. To be published. Preprint available.

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Authors

J. Huang • S. R. Vutukur • P. K. Yu • N. Navab • S. Ilic • B. Busam

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Links

URL GitHub

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Research Areas

 B1 | Computer Vision

 C1 | Medicine