  Understanding physical relations between objects, especially their support
relations, is crucial for robotic manipulation. There has been work on
reasoning about support relations and structural stability of simple
configurations in RGB-D images. In this paper, we propose a method for
extracting more detailed physical knowledge from a set of RGB-D images taken
from the same scene but from different views using qualitative reasoning and
intuitive physical models. Rather than providing a simple contact relation
graph and approximating stability over convex shapes, our method is able to
provide a detailed supporting relation analysis based on a volumetric
representation. Specifically, true supporting relations between objects (e.g.,
if an object supports another object by touching it on the side or if the
object above contributes to the stability of the object below) are identified.
We apply our method to real-world structures captured in warehouse scenarios
and show our method works as desired.
