Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments

For the reconstruction of objects during hand–object interactions, accurate pose estimation is indispensable. By improving the precision of pose estimation, the accuracy of the 3D reconstruction results can be enhanced. Recently, pose tracking techniques are no longer limited to individual objects, leading to advancements in the reconstruction of objects interacting with other objects. However, most methods struggle to handle incomplete target information in complex scenes and mutual interference between objects in the environment, leading to a decrease in pose estimation accuracy. We proposed an improved algorithm building upon the existing BundleSDF framework, which enables more robust and accurate tracking by considering the occlusion relationships between objects. First of all, for detecting changes in occlusion relationships, we segment the target and compute dual-layer masks. Secondly, rough pose estimation is performed through feature matching, and a keyframe pool is introduced for pose optimization, which is maintained based on occlusion relationships. Lastly, the estimated results of historical frames are used to train an object neural field to assist in the subsequent pose-tracking process. Experimental verification shows that on the HO-3D dataset, our method can significantly improve the accuracy and robustness of object tracking in frequent interactions, providing new ideas for object pose-tracking tasks in complex scenes.