  Most of the approaches for indoor RGBD semantic la- beling focus on using
pixels or superpixels to train a classi- fier. In this paper, we implement a
higher level segmentation using a hierarchy of superpixels to obtain a better
segmen- tation for training our classifier. By focusing on meaningful segments
that conform more directly to objects, regardless of size, we train a random
forest of decision trees as a clas- sifier using simple features such as the 3D
size, LAB color histogram, width, height, and shape as specified by a his-
togram of surface normals. We test our method on the NYU V2 depth dataset, a
challenging dataset of cluttered indoor environments. Our experiments using the
NYU V2 depth dataset show that our method achieves state of the art re- sults
on both a general semantic labeling introduced by the dataset (floor,
structure, furniture, and objects) and a more object specific semantic
labeling. We show that training a classifier on a segmentation from a hierarchy
of super pixels yields better results than training directly on super pixels,
patches, or pixels as in previous work.
