3D-Aware Hypothesis & Verification for Generalizable Relative Object
  Pose Estimation
Chen Zhao Tong Zhang Mathieu Salzmann
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.