TerraMobilita/iQmulus urban point cloud analysis benchmark

The objective of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser scanning (MLS) at large scale. A very detailed semantic tree for urban scenes is proposed. We call analysis the capacity of a method to separat...

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Published inComputers & graphics Vol. 49; pp. 126 - 133
Main Authors Vallet, Bruno, Brédif, Mathieu, Serna, Andres, Marcotegui, Beatriz, Paparoditis, Nicolas
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2015
Elsevier
SeriesComputers and Graphics
Subjects
Benchmark
Benchmarking
Categories
Classification
Computer Science
Ground truth
Image Processing
Laser scanning
Lasers
Mobile mapping
Scene analysis
Segmentation
Semantics
Three dimensional models
Urban scene
Benchmark
Mobile mapping
Laser scanning
Segmentation
Urban scene
Classification
detection
urban scene
segmentation
mobile mapping
classification
analysis
laser scanning
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Abstract The objective of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser scanning (MLS) at large scale. A very detailed semantic tree for urban scenes is proposed. We call analysis the capacity of a method to separate the points of the scene into these categories (classification), and to separate the different objects of the same type for object classes (detection). A very large ground truth is produced manually in two steps using advanced editing tools developed especially for this benchmark. Based on this ground truth, the benchmark aims at evaluating the classification, detection and segmentation quality of the submitted results. [Display omitted] •Very rich data: high accuracy, high resolution, many attributes.•Massive data: 160 million annotated points thanks to a performant web based annotation tool (and many hours of work).•Rich semantics organized in a semantic tree with various levels of generalization.•Very objective evaluation metrics.
AbstractList The objective of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser scanning (MLS) at large scale. A very detailed semantic tree for urban scenes is proposed. We call analysis the capacity of a method to separate the points of the scene into these categories (classification), and to separate the different objects of the same type for object classes (detection). A very large ground truth is produced manually in two steps using advanced editing tools developed especially for this benchmark. Based on this ground truth, the benchmark aims at evaluating the classification, detection and segmentation quality of the submitted results. [Display omitted] •Very rich data: high accuracy, high resolution, many attributes.•Massive data: 160 million annotated points thanks to a performant web based annotation tool (and many hours of work).•Rich semantics organized in a semantic tree with various levels of generalization.•Very objective evaluation metrics.
The object of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser scanning (MLS) at large scale. A very detailed semantic tree for urban scenes is proposed. We call analysis the capacity of a method to separate the points of the scene into these categories (classification), and to separate the different objects of the same type for object classes (detection). A very large ground truth is produced manually in two steps using advanced editing tools developed especially for this benchmark. Based on this ground truth, the benchmark aims at evaluating both the classification, detection and segmentation quality of the submitted results.
The objective of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser scanning (MLS) at large scale. A very detailed semantic tree for urban scenes is proposed. We call analysis the capacity of a method to separate the points of the scene into these categories (classification), and to separate the different objects of the same type for object classes (detection). A very large ground truth is produced manually in two steps using advanced editing tools developed especially for this benchmark. Based on this ground truth, the benchmark aims at evaluating the classification, detection and segmentation quality of the submitted results.
Author Vallet, Bruno
Brédif, Mathieu
Marcotegui, Beatriz
Serna, Andres
Paparoditis, Nicolas
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  organization: Université Paris-Est, IGN Recherche, SRIG, MATIS, 73 avenue de Paris, 94160 Saint Mandé, France
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Keywords Benchmark
Mobile mapping
Laser scanning
Segmentation
Urban scene
Classification
detection
urban scene
segmentation
mobile mapping
classification
analysis
laser scanning
Language English
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Snippet The objective of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser...
The object of the TerraMobilita/iQmulus 3D urban analysis benchmark is to evaluate the current state of the art in urban scene analysis from mobile laser...
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StartPage 126
SubjectTerms Benchmark
Benchmarking
Categories
Classification
Computer Science
Ground truth
Image Processing
Laser scanning
Lasers
Mobile mapping
Scene analysis
Segmentation
Semantics
Three dimensional models
Urban scene
Title TerraMobilita/iQmulus urban point cloud analysis benchmark
URI https://dx.doi.org/10.1016/j.cag.2015.03.004
https://www.proquest.com/docview/1753541375
https://hal.science/hal-01167995
Volume 49
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