Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

Blog Article

3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow.Point clouds, which could be either derived from passive or active imaging Skinny systems, are an important source for 3D modeling.Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process.

Segmentation is usually the first step in the data processing chain.This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e.

, local point density/spacing and noise level) of the point cloud in question.The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing.Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features.

Starting from optimally-selected seed regions, planar and pole-like features are then segmented.The paper also Munsters introduces a list of hypothesized artifacts/problems that might take place during the region-growing process.Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features.

Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.