Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. In the mean while, we propose a new metric named Infimum ADD (IADD) which is an invariant measurement for objects with different types of pose ambiguity. A two-stage baseline solution for this task is also provided. By training an endto-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently. It then calculates the 6D pose from the correspondences using an algorithm robust to object symmetry. Extensive experiments show that our method outperforms several intuitive baselines and thus verify its effectiveness. All the data, code and models will be made publicly available. Project page: www.graspnet.net/unseen6d

In this paper, we introduce a new method for the spacetime registration of a growing plant that is based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into parts that we call branches. Then these branches are further divided into smaller segments that possess a simple geometric structure. These segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP.We applied our method to various types of plants, including arabidopsis, tomato plant and maize. We established three different metrics for 3D point-wise shape correspondence to test the accuracy, continuity, and cycle consistency of the mapping. We then compared our method with the state-of-the-art. Our results show that our approach achieves better or similar results with a shorter running time.

Y3Fe5O12 (YIG) and BiY2Fe5O12 (Bi:YIG) films are epitaxially grown on a series of (111)-oriented garnets substrates using pulsed laser deposition. Structural and ferromagnetic resonance characterizations demonstrate the high epitaxial quality, extremely low magnetic loss, and coherent strain state in these films. Using these epitaxial films as model systems, we systematically investigate the evolutions of magnetic anisotropy (MA) with epitaxial strain and chemical doping. For both the YIG and Bi:YIG films, the compressive strain tend to align the magnetic moment in film plane, while the tensile strain can compete with the demagnetization effect and stabilize a perpendicular MA. We found that the strain induced lattice elongation/compression along the out-of-plane[111] axis is the key parameter that determines the MA. More importantly, the strain-induced tunability of MA can be enhanced significantly by Bi doping, and meanwhile, the ultra-low damping feature persists. We clarified that the cooperation between strain and chemical doping could realize an effective control of MA in garnet-type ferrites, which is essential for spintronic applications.