Automatic Extraction of Building Roof Planes From Airborne Lidar Data Applying an Extended 3d Randomized Hough Transform

Abstract

ABSTRACT:This study aims to extract automatically building roof planes from airborne LIDAR data applying an extended 3D Randomized Hough Transform (RHT). The proposed methodology consists of three main steps, namely detection of building points, plane detection and refinement. For the detection of the building points, the vegetative areas are first segmented from the scene content and the bare earth is extracted afterwards. The automatic plane detection of each building is performed applying extensions of the RHT associated with additional constraint criteria during the random selection of the 3 points aiming at the optimum adaptation to the building rooftops as well as using a simple design of the accumulator that efficiently detects the prominent planes. The refinement of the plane detection is conducted based on the relationship between neighbouring planes, the locality of the point and the use of additional information. An indicative experimental comparison to verify the advantages of the extended RHT compared to the 3D Standard Hough Transform (SHT) is implemented as well as the sensitivity of the proposed extensions and accumulator design is examined in the view of quality and computational time compared to the default RHT. Further, a comparison between the extended RHT and the RANSAC is carried out. The plane detection results illustrate the potential of the proposed extended RHT in terms of robustness and efficiency for several applications.

Multifaceted Geometric Assessment towards Simplified Urban Surfaces Built by 3D Reconstruction

“…Since the objective of an assessment method is based on surface classification, before our multifaceted error measurement, this section provides a brief overview of surface classification methods. The methods for classifying surfaces include random sample consensus, Hough transform [32], and setting eigenvalue thresholds for neighbor matrices [33]. They are typically used for pattern recognition.…”

Automated Extraction of Heavyweight and Lightweight Models of Urban Features from LiDAR Point Clouds by Specialized Web-Software

“…The AFE complete algorithmic content implies the ground and vegetation detection, while the man-made feature AFE-technique has to use either single-, or multi-return ALS / MSL / UAV-LS range and intensity information with application of the various thematic algorithms, e.g., as: neural networks [61]; RANSAC (the RANdom SAmple Consensus algorithm) approach for extraction of feature plains [62] with its key modifications [63,64], that have been successfully employed by the Polygonal Surface Reconstruction method (the PolyFit) for feature reconstruction [65]; 3D Standard / Randomized Hough Transform (SHT / RHT) methodology that generally consists of three main steps: building points' detection, detection of building planes, and these planes' refinement [66][67][68]; implementation of knowledge-based entities [69]; the multi-scale approach [70].…”

Active Sampling and Model Based Prediction for Fast and Robust Detection and Reconstruction of Complex Roofs in 3d Point Clouds

“…In data-driven approaches, geometric primitives such as planes are segmented in a first step and aggregated afterwards. Popular methods are among others RANSAC (Tarsha-Kurdi et al, 2008), 3D-Hough-transform (Maltezos, Ioannidis, 2016), graph matching (Oude Elberink, 2009) and region growing (Rottensteiner, 2006). A further data driven approach is presented by (Poullis, 2013).…”