Arbitrary 3D object extraction from cluttered laser scans using local features

Determination of construction performance metrics requires intensive processing of large amounts of data collected on construction sites including cluttered laser scans. For example, for quality control of construction components using 3D laser scans, the acquired point cloud should be cleaned and the object-of-interest should be extracted for measuring the incurred deviations. Such a procedure is tedious, time consuming and inaccurate due to intensive manual user operations. Although automatic extraction of rough and simple 3D shapes and features is performed by applying techniques such as Hough transform, automatic extraction of construction components with complex geometry is a challenging research need that must be addressed for fully automated modelling and processing. This paper presents a framework for automated extraction of 3D objects with arbitrary shapes and geometry. A new local feature set, which is globally invariant, is created in order to represent 3D models. The feature space created is then searched for in the cluttered laser scan by hashing from a hash table created for the 3D model. The best match is then extracted automatically by applying a post-processing RANSAC loop. The framework is then followed by an ICP-based registration in order to refine the best match identified. The results show that the method is sufficiently robust and quick to be applied for effective and efficient post processing of the laser scans acquired on construction sites.