Physical Plausibility of 6D Pose Estimates in Scenes of Static Rigid Objects

Dominik Bauer, Timothy Patten, Markus Vincze

Abstract: To enable robots to reason about manipulation of objects and AR applications to present augmented scenes to human users, accurate scene explanations based on objects and their 6D pose are required. With the pose-error functions commonly used to evaluate 6D object pose estimation approaches, the accuracy of estimates is measured by surface alignment of a target object under the estimated and true pose. However, an object floating above the ground may yield the same error as an object translated on the ground by the same magnitude. We argue that, to be intelligible for human observers, pose estimates additionally need to adhere to physical principles. To this end, we provide a definition of physical plausibility in scenes of static rigid objects, derive novel pose-error functions and compare them to existing evaluation approaches in 6D object pose estimation. Code to compute the presented pose-error functions is publicly available at github.com/dornik/plausible-poses .