RGB-D cameras are not only able to provide color (Red-Green-Blue -RGB-) informa-
tion from the scene but also a relatively accurate cloud of 3D points. Using information
coming from this organized cloud, it is possible to define around each image pixel a small
planar patch and obtain its normal vector. Within the framework of the combinatorial
pyramid, this paper describes a method to abstract from these normals to paramet-
ric surface models. The method works at two consecutive stages. Firstly, normals
are hierarchically grouped to divide up the image into superpixels. These superpixels
capture small patches on the scene that belong to the same surface. Then, they are
merged to segment the scene into simple geometric models. Curvature information
and model information are used to divide up the image into planes, cylinders and/or
spheres. This paper shows how, in the higher levels of abstraction of the combinatorial
pyramid, scenes can be described using these geometric items and their topological
relationships.
