RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects

TL;DR

RefPose introduces a novel method for 6D pose estimation of unseen objects by leveraging reference images and geometric correspondences, using a render-and-compare refinement process with correlation-guided attention, achieving state-of-the-art results.

Contribution

The paper presents a new approach that dynamically adapts to unseen objects using reference images and geometric correspondence, improving pose estimation accuracy.

Findings

Achieves state-of-the-art performance on BOP datasets.

Robustly estimates poses for unseen objects.

Maintains competitive runtime performance.

Abstract

Estimating the 6D pose of unseen objects from monocular RGB images remains a challenging problem, especially due to the lack of prior object-specific knowledge. To tackle this issue, we propose RefPose, an innovative approach to object pose estimation that leverages a reference image and geometric correspondence as guidance. RefPose first predicts an initial pose by using object templates to render the reference image and establish the geometric correspondence needed for the refinement stage. During the refinement stage, RefPose estimates the geometric correspondence of the query based on the generated references and iteratively refines the pose through a render-and-compare approach. To enhance this estimation, we introduce a correlation volume-guided attention mechanism that effectively captures correlations between the query and reference images. Unlike traditional methods that depend on pre-defined object models, RefPose dynamically adapts to new object shapes by leveraging a reference image and geometric correspondence. This results in robust performance across previously unseen objects. Extensive evaluation on the BOP benchmark datasets shows that RefPose achieves state-of-the-art results while maintaining a competitive runtime.