Conference Paper

Surface Reconstruction from LiDAR Data with Extended Snake Theory

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Abstract

Surface reconstruction from implicit data of sub-randomly distributed 3D points is the key work of extracting explicit information from LiDAR data. This paper proposes an approach of extended snake theory to surface reconstruction from LiDAR data. The proposed algorithm approximates a surface with connected planar patches. Growing from an initial seed point, a surface is reconstructed by attaching new adjacent planar patches based on the concept of minimizing the deformable energy. A least-squares solution is sought to keep a local balance of the internal and external forces, which are inertial forces maintaining the flatness of a surface and pulls of observed LiDAR points bending the growing surface toward observations. Experiments with some test data acquired with a ground-based LiDAR demonstrate the feasibility of the proposed algorithm. The effects of parameter settings on the delivered results are also investigated.

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... When this data is studied interactively it is easy to form a mental image of the geometry of the scene. The collection of 3D points gives insight into the surfaces that were sampled, and many methods have been proposed to automatically detect these surfaces [12] [14]. ...
... When this data is studied interactively it is easy to form a mental image of the geometry of the scene. The collection of 3D points gives insight into the surfaces that were sampled, and many methods have been proposed to automatically detect these surfaces [12, 14]. One influential group of methods is the methods based on RANSAC [3] , which uses a minimal random sample to define a surface and iterates to find the surface containing most points. ...
... One influential group of methods is the methods based on RANSAC [3] , which uses a minimal random sample to define a surface and iterates to find the surface containing most points. Another group of methods is based on region growing [14], which initializes a bounded surface at a certain point and tries to expand this while maintaining some constraint like maximal deviation from the surface in location or normal. Finally , Funke, Malamatos, and Ray [4] give an approximation algorithm for finding a connected component with comparable normals in a triangulation. ...
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... While interactive methods like SmartBoxes [14] significantly speed up manual reconstruction, their reliance on a human operator makes them less appropriate for handling massive datasets within limited time. Recent research into automatic geometry reconstruction from laser range scans has focused on smooth sur- faces [2, 11, 20] . A likely reason for using smooth surfaces is that traditionally most high-density laser range scans were made using close range measurements of natural objects in a controlled environment. ...
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... The angular difference between the two unit normal vectors ̃ and ̃ are calculated by computing the inverse cosine of the dot product of the two vectors as in Equation (1) [14], [23], [24]. The angle can be in the range from the degree of 0 o to 90 o , corresponding from the lowest dissimilarity to the highest dissimilarity between the tendencies of the two planes. ...
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... Apart from digital images, few methods based on active contour models have also been developed for extracting features from LiDAR data. [40] proposed an approach for surface reconstruction from LiDAR data. In their algorithm, a surface was grown from an initial seed point in the LiDAR data based on the extended snake model. ...
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Active contour models present a robust segmentation approach which make efficient use of specific information about objects in the input data rather than processing all the data. They have been widely used in many applications including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting roads from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation are discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour based methodologies which have been developed to extract roads and other features from LiDAR and digital imaging datasets. We present a small case study in which an integrated version of active contour models is applied to automatically extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides a valuable insight on the potential of active contours for extracting roads and other infrastructures from 3D LiDAR point cloud data.
... If θ is found to be greater than 90, the value of θ is replaced with the value that is supplementary of itself. The value of θ ranges from 0 to 90 ( Tseng, Tang, & Chou, 2007 ). ...
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... Second, for the growing criteria, the similarity of normal vectors and the distance between neighbor points and the current region are widely adopted [30][31][32]. Alternatively, neighboring patches can be applied to growing regions [33] based on minimizing the deformable energy. Third, as the region grows larger, the fitting error also monotonically increases, and the termination criteria are determined by the largest error. ...
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Surface Clustering from Airborne Laser Scanning Data. International Archives of Photogrammetry and Remote Sensing
  • S Filin
  • S. Filin
Dynamic 3D models with local and global deformations: deformable Superquadrics
  • G Terzopoulous
  • D Metaxas
  • G. Terzopoulous