Novel Object 6D Pose Estimation with a Single Reference View
This addresses the scalability issue in robotics and computer vision by reducing the need for CAD models or multiple views, though it is incremental as it builds on existing pose estimation methods.
The paper tackles the problem of 6D pose estimation for novel objects using only a single reference view, which is more scalable but challenging due to large pose discrepancies and limited information, and achieves on-par performance with CAD-based and dense reference view-based methods in experiments on six datasets and real-world robotic scenes.
Existing novel object 6D pose estimation methods typically rely on CAD models or dense reference views, which are both difficult to acquire. Using only a single reference view is more scalable, but challenging due to large pose discrepancies and limited geometric and spatial information. To address these issues, we propose a Single-Reference-based novel object 6D (SinRef-6D) pose estimation method. Our key idea is to iteratively establish point-wise alignment in a common coordinate system based on state space models (SSMs). Specifically, iterative object-space point-wise alignment can effectively handle large pose discrepancies, while our proposed RGB and Points SSMs can capture long-range dependencies and spatial information from a single view, offering linear complexity and superior spatial modeling capability. Once pre-trained on synthetic data, SinRef-6D can estimate the 6D pose of a novel object using only a single reference view, without requiring retraining or a CAD model. Extensive experiments on six popular datasets and real-world robotic scenes demonstrate that we achieve on-par performance with CAD-based and dense reference view-based methods, despite operating in the more challenging single reference setting. Code will be released at https://github.com/CNJianLiu/SinRef-6D.