Conditional Random Field for per-line classification of laser point clouds

English Abstract:

In recently years, laser scanner rapidly becomes a primary acquisition tool due to its fast acquisition of massive three-dimensional point clouds. For fully utilizing its benefits, developing a robust method to classify many objects of interests from laser point clouds is urgently required. Conditional Random Field (CRF) is a well-known discriminative classifier, which integrates local appearance of the observation with spatial interactions among its neighbouring points in classification process. Typical CRFs employ generic label consistency using short-range dependency only, which often causes locality problem. In this paper, we present a multi-range and asymmetric Conditional Random Field (CRF) (maCRF), which adopts a priori information of scene-layout compatibility addressing long-range dependency. The proposed CRF constructs two graphical models, one for enhancing a local labelling smoothness within short-range (srCRF) and the other for favouring a global and asymmetric regularity of spatial arrangement between different object classes within long-range (lrCRF). This maCRF classifier assumes two graphical models (srCRF and lrCRF) are independent of each other. Final labelling decision was accomplished by probabilistically combining prediction results obtained from two CRF models. We validated maCRF’s performance with TLS point clouds acquired from RIEGL LMS-Z390i scanner using cross validation. Experiment results demonstrate that synergetic classification improvement can be achievable by incorporating two CRF models. The presentation will extend it to enabling the proposed Conditional Random Field (CRF) to classify laser point clouds not only in along scannding direction, but also across scanning direction as well. Also, the presentation will demonstrate its application of both static and mobile laser scanning data.