Abstract
Abstract. Terrestrial Laser Scanning (TLS) rapidly becomes a primary surveying tool due to its fast acquisition of highly dense threedimensional point clouds. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. Conditional Random Field (CRF) is a well-known discriminative classifier, which integrates local appearance of the observation (laser point) 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.
Highlights
Terrestrial Laser Scanning (TLS) is a relatively new surveying tool, but has been rapidly adopted for modelling urban street scenes
To overcome this locality problem, we constructs two graphical models, one for enhancing a local consistency over labels connected within short-range dense graphical model and the other for favouring a global regularity of spatial arrangement between different object classes that is structured within a sparse longrange graphical model
We observed that context-based classifiers shows better classification performance compared to local classifier (GMM-Expectation Maximization (EM))
Summary
Terrestrial Laser Scanning (TLS) is a relatively new surveying tool, but has been rapidly adopted for modelling urban street scenes. A typical approach to the classification problem is to identify a target object by only relying on local apparent features differentiating the object from the others This naïve method often causes classification errors due to ambiguities in appearance among classes in varying vision conditions. Typical CRF only works locally as it emphasizes on local label consistency, not capturing its global properties To overcome this locality problem, we constructs two graphical models, one for enhancing a local consistency over labels connected within short-range dense graphical model (srCRF) and the other for favouring a global regularity of spatial arrangement between different object classes that is structured within a sparse longrange graphical model (lrCRF). Final classification was accomplished by combining their label prediction results This ensemble classification system synergistically compensates the limitations caused by srCRF (“over-smoothing”) and lrCRF (“under-smoothing) respectively.
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