Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods

Biosca, Josep Miquel; Lerma, José Luis

doi:10.1016/j.isprsjprs.2007.07.010

Cited by 164 publications

(81 citation statements)

References 15 publications

(16 reference statements)

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“…However, the weakness of this approach is connected with a proper choice of the seed points. Furthermore, selecting different seed points may results different segmentation regions [4]. In addition, this algorithm tends to generate distorted boundaries due to segment objects in the region level instead of pixel level [5].…”

Section: Related Workmentioning

confidence: 99%

Extended Hybrid Region Growing Segmentation of Point Clouds with Different Resolution from Dense Aerial Image Matching

Omidalizarandi¹,

Saadatseresht²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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In the recent years, 3D city reconstruction is one of the active researches in the field of photogrammetry. The goal of this work is to improve and extend region growing based segmentation in the X-Y-Z image in the form of 3D structured data with combination of spectral information of RGB and grayscale image to extract building roofs, streets and vegetation. In order to process 3D point clouds, hybrid segmentation is carried out in both object

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Section: Related Workmentioning

confidence: 99%

Extended Hybrid Region Growing Segmentation of Point Clouds with Different Resolution from Dense Aerial Image Matching

Omidalizarandi¹,

Saadatseresht²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Applications usually require extensive processing of point cloud for extracting information on the features of interest to derive the final product (Biosca and Lerma, 2008;Vosselman et al, 2004). Analysis options with a set of unstructured 3D points are limited (Biosca and Lerma, 2008).…”

Section: Motivationmentioning

confidence: 99%

“…Analysis options with a set of unstructured 3D points are limited (Biosca and Lerma, 2008). Apart from the fact that the point data are noisy and not perfectly sampled, lidar acquisitions may also have poor sampling for almost vertical scans (Golovinsky and Funkhouser, 2009).…”

Section: Motivationmentioning

confidence: 99%

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Min-Cut Based Segmentation of Airborne Lidar Point Clouds

Ural

Shan

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Introducing an organization to the unstructured point cloud before extracting information from airborne lidar data is common in many applications. Aggregating the points with similar features into segments in 3-D which comply with the nature of actual objects is affected by the neighborhood, scale, features and noise among other aspects. In this study, we present a min-cut based method for segmenting the point cloud. We first assess the neighborhood of each point in 3-D by investigating the local geometric and statistical properties of the candidates. Neighborhood selection is essential since point features are calculated within their local neighborhood. Following neighborhood determination, we calculate point features and determine the clusters in the feature space. We adapt a graph representation from image processing which is especially used in pixel labeling problems and establish it for the unstructured 3-D point clouds. The edges of the graph that are connecting the points with each other and nodes representing feature clusters hold the smoothness costs in the spatial domain and data costs in the feature domain. Smoothness costs ensure spatial coherence, while data costs control the consistency with the representative feature clusters. This graph representation formalizes the segmentation task as an energy minimization problem. It allows the implementation of an approximate solution by min-cuts for a global minimum of this NP hard minimization problem in low order polynomial time. We test our method with airborne lidar point cloud acquired with maximum planned post spacing of 1.4 m and a vertical accuracy 10.5 cm as RMSE. We present the effects of neighborhood and feature determination in the segmentation results and assess the accuracy and efficiency of the implemented min-cut algorithm as well as its sensitivity to the parameters of the smoothness and data cost functions. We find that smoothness cost that only considers simple distance parameter does not strongly conform to the natural structure of the points. Including shape information within the energy function by assigning costs based on the local properties may help to achieve a better representation for segmentation.

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“…All these methodologies have been applied to relatively simple datasets that do not represent the typical complexity of natural scenes. With terrestrial laser scanning data, extraction of planar faces has mostly been considered, e.g., Dold and Brenner [19], Biosca and Lerma [20] who use fuzzy clustering to extract planar surfaces, or Wang and Tseng [21] who use an octree based approach. Gorte [22] extracts planar faces using a panoramic representation of the range data.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees

Barnea

Filin

2012

Remote Sensing

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Abstract:Terrestrial laser scanning is becoming a standard for 3D modeling of complex scenes. Results of the scan contain detailed geometric information about the scene; however, the lack of semantic details still constitutes a gap in ensuring this data is usable for mapping. This paper proposes a framework for recognition of objects in laser scans; aiming to utilize all the available information, range, intensity and color information integrated into the extraction framework. Instead of using the 3D point cloud, which is complex to process since it lacks an inherent neighborhood structure, we propose a polar representation which facilitates low-level image processing tasks, e.g., segmentation and texture modeling. Using attributes of each segment, a feature space analysis is used to classify segments into objects. This process is followed by a fine-tuning stage based on graph-cut algorithm, which considers the 3D nature of the data. The proposed algorithm is demonstrated on tree extraction and tested on scans containing complex objects in addition to trees. Results show a very high detection level and thereby the feasibility of the proposed framework.

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Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods

Cited by 164 publications

References 15 publications

Extended Hybrid Region Growing Segmentation of Point Clouds with Different Resolution from Dense Aerial Image Matching

Extended Hybrid Region Growing Segmentation of Point Clouds with Different Resolution from Dense Aerial Image Matching

Min-Cut Based Segmentation of Airborne Lidar Point Clouds

Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees

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