UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image
This work addresses the costly onboarding stage in robotics and computer vision by enabling pose estimation with minimal reference data, though it is incremental as it builds upon existing coarse-to-fine paradigms.
The paper tackles the problem of estimating the pose of unseen objects using only a single unposed RGB-D reference image, which simplifies reference acquisition compared to methods requiring CAD models or multiple views. It introduces UNOPose, a novel approach that achieves superior performance in the one-reference setting, significantly outperforming traditional and learning-based methods and remaining competitive with CAD-model-based methods.
Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimate the unseen object's pose through a single unposed RGB-D reference image. While previous works leverage reference images as pose anchors to limit the range of relative pose, our scenario presents significant challenges since the relative transformation could vary across the entire SE(3) space. Moreover, factors like occlusion, sensor noise, and extreme geometry could result in low viewpoint overlap. To address these challenges, we present a novel approach and benchmark, termed UNOPose, for unseen one-reference-based object pose estimation. Building upon a coarse-to-fine paradigm, UNOPose constructs an SE(3)-invariant reference frame to standardize object representation despite pose and size variations. To alleviate small overlap across viewpoints, we recalibrate the weight of each correspondence based on its predicted likelihood of being within the overlapping region. Evaluated on our proposed benchmark based on the BOP Challenge, UNOPose demonstrates superior performance, significantly outperforming traditional and learning-based methods in the one-reference setting and remaining competitive with CAD-model-based methods. The code and dataset are available at https://github.com/shanice-l/UNOPose.