About: High Accuracy Terrain Reconstruction from Point Clouds Using Implicit Deformable Model

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	High Accuracy Terrain Reconstruction from Point Clouds Using Implicit Deformable Model
Creator	Bac, Alexandra Kanai, Takashi Morel, Jules
Source	PMC
abstract	Few previous works have studied the modeling of forest ground surfaces from LiDAR point clouds using implicit functions. [10] is a pioneer in this area. However, by design this approach proposes over-smoothed surfaces, in particular in highly occluded areas, limiting its ability to reconstruct fine-grained terrain surfaces. This paper presents a method designed to finely approximate ground surfaces by relying on deep learning to separate vegetation from potential ground points, filling holes by blending multiple local approximations through the partition of unity principle, then improving the accuracy of the reconstructed surfaces by pushing the surface towards the data points through an iterative convection model.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50433-5_20
has license	no-cc
sha1sum (hex)	dd8d95803d8b7f1d07b18f22c58906cbb62e5648
schema:url	https://doi.org/10.1007/978-3-030-50433-5_20
resource representing a document's title	High Accuracy Terrain Reconstruction from Point Clouds Using Implicit Deformable Model
has PubMed Central identifier	PMC7304704
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:dd8d95803d8b7f1d07b18f22c58906cbb62e5648#body_text
is schema:about of	named entity 'point clouds' named entity 'implicit functions' named entity 'point clouds' named entity 'level-set' named entity 'MLS' named entity 'topology' named entity 'radial basis functions' named entity 'surface reconstruction' named entity 'partition of unity' named entity 'compactly supported' named entity 'terrain modeling' named entity 'point cloud' named entity 'point cloud' named entity '3D scans' named entity '3D models' named entity 'Digital terrain model' named entity 'extreme values' named entity 'Euler's method' named entity 'orthogonal' named entity 'deep learning' named entity 'minimum bounding rectangle' named entity 'data structure' named entity 'LiDAR' named entity 'implicit function' named entity 'velocity vector' named entity 'deep network' named entity 'LiDAR' named entity 'quad-tree' named entity 'point clouds' named entity 'data-sets' named entity 'deep learning' named entity 'SpeedTree' named entity 'sampling rates' named entity 'quad-tree' named entity 'TLS' named entity 'deep network' named entity 'RBF' named entity 'gradient' named entity 'voxel' named entity 'data processing' named entity 'partial differential equation' named entity 'surface gradient' named entity 'sensor' named entity 'Gaussian noise' named entity 'nzf' named entity 'DTM' named entity 'RBF' named entity 'level-set' named entity 'point cloud' named entity 'implicit function' named entity 'level-set' named entity 'PCA' named entity 'algorithm' named entity 'quadric' named entity 'remote sensing' named entity 'PDE' named entity 'deep learning' named entity 'implicit function' named entity 'compactly supported' named entity 'Cholesky decomposition' named entity 'LiDAR' named entity 'TLS' named entity 'parametric surface' named entity 'quad-tree' named entity 'centroid' named entity 'tangent plane' named entity 'DTM' named entity 'Cholesky factorization' named entity 'quadtree' named entity 'point cloud' named entity 'level-sets' named entity 'point clouds' named entity 'DTMs' named entity 'eigenvalues' named entity 'point clouds' named entity 'previous Section' named entity 'quad-tree' named entity 'octree' named entity 'topography' named entity 'laser scan' named entity 'point clouds'

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