RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation
This work addresses the challenge of unseen object pose estimation for robotics and computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of inaccurate pose predictions in template-based 6D object pose estimation by reformulating it as a ray alignment problem, using a diffusion transformer to align query images with posed templates, and achieves competitive results on benchmark datasets.
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.