  We pose 3D scene-understanding as a problem of parsing in a grammar. A
grammar helps us capture the compositional structure of real-word objects,
e.g., a chair is composed of a seat, a back-rest and some legs. Having multiple
rules for an object helps us capture structural variations in objects, e.g., a
chair can optionally also have arm-rests. Finally, having rules to capture
composition at different levels helps us formulate the entire scene-processing
pipeline as a single problem of finding most likely parse-tree---small segments
combine to form parts of objects, parts to objects and objects to a scene. We
attach a generative probability model to our grammar by having a
feature-dependent probability function for every rule. We evaluated it by
extracting labels for every segment and comparing the results with the
state-of-the-art segment-labeling algorithm. Our algorithm was outperformed by
the state-or-the-art method. But, Our model can be trained very efficiently
(within seconds), and it scales only linearly in with the number of rules in
the grammar. Also, we think that this is an important problem for the 3D vision
community. So, we are releasing our dataset and related code.
