Prior methods that tackle the problem of generalizable object pose estimation
highly rely on having dense views of the unseen object. By contrast, we address
the scenario where only a single reference view of the object is available. Our
goal then is to estimate the relative object pose between this reference view
and a query image that depicts the object in a different pose. In this
scenario, robust generalization is imperative due to the presence of unseen
objects during testing and the large-scale object pose variation between the
reference and the query. To this end, we present a new
hypothesis-and-verification framework, in which we generate and evaluate
multiple pose hypotheses, ultimately selecting the most reliable one as the
relative object pose. To measure reliability, we introduce a 3D-aware
verification that explicitly applies 3D transformations to the 3D object
representations learned from the two input images. Our comprehensive
experiments on the Objaverse, LINEMOD, and CO3D datasets evidence the superior
accuracy of our approach in relative pose estimation and its robustness in
large-scale pose variations, when dealing with unseen objects.

通过单个参考视图与查询图像之间的相对物体姿态估计，我们提出了一种基于假设和验证框架的新方法，并引入了一种 3D 感知验证，以衡量可靠性，证明了我们方法在相对姿态估计和处理未见过的物体时的鲁棒性。