BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation
This addresses the challenge of robust object pose estimation for robotics and AR/VR applications in real-world conditions, representing an incremental improvement over existing methods.
The paper tackles the problem of generalizable object pose estimation in sparse-view and occluded scenarios by introducing object bounding box corners as an intermediate representation, achieving state-of-the-art performance on YCB-Video and Occluded-LINEMOD datasets.
This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications.