In this paper, we introduce a novel, truly prior-free 3D object tracking method that operates without given any model or training priors. Unlike existing methods that typically require pre-defined 3D models or specific training datasets as priors, which limit their applicability, our method is free from these constraints. Our method consists of a geometry generation module and a pose optimization module. Its core idea is to enable these two modules to automatically and iteratively enhance each other, thereby gradually building all the necessary information for the tracking task. We thus call the method as Bidirectional Iterative Tracking(BIT). The geometry generation module starts without priors and gradually generates high-precision mesh models for tracking, while the pose optimization module generates additional data during object tracking to further refine the generated models. Moreover, the generated 3D models can be stored and easily reused, allowing for seamless integration into various other tracking systems, not just our methods. Experimental results demonstrate that BIT outperforms many existing methods, even those that extensively utilize prior knowledge, while BIT does not rely on such information. Additionally, the generated 3D models deliver results comparable to actual 3D models, highlighting their superior and innovative qualities. The code is available at https://github.com/songxiuqiang/BIT.git.

The 3D perception of satellites, including both their shape and pose, is a key foundation for robotic on-orbit servicing. However, the demanding space environment-such as intense and dim illumination-presents significant challenges. Previous non-cooperative methods focus on specific geometric features like solar panel brackets or docking rings, overlooking the satellite's overall shape and increasing the risk of collisions during grasping. Additionally, satellites are often weakly textured, limiting the accuracy of 3D perception. To address these issues, we propose, for the first time, a 3D perceptionbased visual servo system of non-cooperative satellites. This system combines reconstruction and tracking to enhance shape perception and pose estimation accuracy in orbital conditions. Specifically, we employ an alternating iterative strategy to simultaneously reconstruct and track the satellite and introduce a novel constraint to fuse different cues under extreme conditions. Further, we develop a simulation environment platform, a dualarm microgravity grasping system, and an online monitoring module to enhance system capabilities for on-orbit servicing. Synthetic and real-world datasets from the simulation environment are also created for experimental validation. Results show that each module of our system achieves state-of-the-art performance.

In AR applications, the jitter of virtual objects can weaken the sense of integration with the real environment. This jitter is often caused by noise in the pose obtained by 3D tracking or localization methods, especially in monocular vision systems without IMU support. Filtering the pose is an effective method to eliminate jitter, however, it can also cause significant lag in the filtered pose, seriously degrading the AR experience. Existing filters struggle to simultaneously reduce jitter while maintaining low lag. In this paper, we propose a novel Minilag filter, which achieves excellent pose smoothing while significantly reducing the lag through backtracking update and compensation strategies, and has excellent real-time performance. We represent the rotation in the pose in the Lie algebra and filter it in locally Euclidean space, ensuring that the filtering of rotation is consistent with that of vectors. We also analyze the noise distribution and characteristics in the tracked pose, providing a theoretical basis for setting filter parameters. We evaluated the proposed filter using both objective mathematical metrics and a user study, and the experimental results demonstrate that our method achieves state-of-the-art performance.

Abstract Visual monocular 6D pose tracking methods for textureless or weakly‐textured objects heavily rely on contour constraints established by the precise 3D model. However, precise models are not always available in reality, and rough models can potentially degrade tracking performance and impede the widespread usage of 3D object tracking. To address this new problem, we propose a novel tracking method that handles rough models. We reshape the rough contour through the probability map, which can avoid explicitly processing the 3D rough model itself. We further emphasize the inner region information of the object, where the points are sampled to provide color constrains. To sufficiently satisfy the assumption of small displacement between frames, the 2D translation of the object is pre‐searched for a better initial pose. Finally, we combine constraints from both the contour and inner region to optimize the object pose. Experimental results demonstrate that the proposed method achieves state‐of‐the‐art performance on both roughly and precisely modeled objects. Particularly for the highly rough model, the accuracy is significantly improved (40.4% v.s. 16.9%).