Decomposition Of Range Images Using Markov Random Fields

This paper describes a computational model for deriving a decomposition of objects from laser rangefinder data. The process aims to produce a set of parts defined by compactness and smoothness of surface connectivity. Relying on a general decomposition rule, any kind of objects made up of free-form surfaces are partitioned. A robust method to partition the object based on Markov Random Fields (MRF), which allows to incorporate prior knowledge, is presented. Shape index and curvedness descriptors along with discontinuity and concavity distributions are introduced to classify region labels correctly. In addition, a novel way to classify the shape of a surface is proposed resulting in a better distinction of concave, convex and saddle shapes. To achieve a reliable classification a multi-scale method provides a stable estimation of the shape index