Detecting, Fitting, and Classifying Surface Primitives for Infrastructure Point Cloud Data

Abstract

This paper presents a novel algorithm for detecting, fitting and classifying the embedded surface primitives from a point cloud dataset (PCD). Given a noisy infrastructure PCD the final output of the algorithm consists of segmented surfaces, their estimated quadric models and corresponding surface classification. Initially, the PCD is down-sampled with a k-d tree structure then segmented via subspace learning. After pose recovery for each segmented group via singular value decomposition, a full quadric model is fit in MLESAC using the direct linear transform for parameter estimation. From the model parameters, the surface is classified from the rank, determinant, and eigenvalues of the parameter matrices. Finally model merging is performed to simplify the results. A real-world PCD of a bridge is used to test the algorithm. The experimental validation of the algorithm demonstrates that the surface primitives are accurately estimated and classified. INTRODUCTION The conditions of a facility during or after construction are not always truthfully represented by the as-designed documentation. In contrast, the as-built building information models (BIMs) do describe an existing facility more accurately. A typical process to generate as-built BIMs uses raw PCD from remote-sensing or photogrammetry as input and outputs an information-rich object oriented models. However, currently generating as-built BIMs is cost-prohibitive, because the conversion from the raw PCDs to the geometric models is a very time-consuming manual process. Therefore, automating this conversion process is of great significance for greater use of as-built BIMs. To address this problem, Zhang (Zhang, G., et al, 2012) proposed a sparsityinducing optimization based algorithm to extract planar patches from noisy PCDs. The algorithm first segments the PCD into linear subspaces and then performs robust model estimation with planar models. This approach has been demonstrated to be effective for real-world infrastructure PCDs. However, it only works for planar surfaces. In contrast, this work seeks to extract more kinds of structures from infrastructure PCDs. This work focuses on processing PCDs instead of the registered images to make the algorithm applicable to not only image-based reconstructed PCDs but also PCDs from laser scanners. The algorithm is designed to detect, fit, and