A Pipeline for the Segmentation and Classification of 3D Point Clouds View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

B. Douillard , J. Underwood , V. Vlaskine , A. Quadros , S. Singh

ABSTRACT

This paper presents algorithms for fast segmentation of 3D point clouds and subsequent classification of the obtained 3D segments. The method jointly determines the ground surface and segments individual objects in 3D, including overhanging structures. When compared to six other terrain modelling techniques, this approach has minimal error between the sensed data and the representation; and is fast (processing a Velodyne scan in approximately 2 seconds). Applications include improved alignment of successive scans by enabling operations in sections (Velodyne scans are aligned 7% sharper compared to an approach using raw points) and more informed decision-making (paths move around overhangs). The use of segmentation to aid classification through 3D features, such as the Spin Image or the Spherical Harmonic Descriptor, is discussed and experimentally compared. Moreover, the segmentation facilitates a novel approach to 3D classification that bypasses feature extraction and directly compares 3D shapes via the ICP algorithm. This technique is shown to achieve accuracy on par with the best feature based classifier (92.1%) while being significantly faster and allowing a clearer understanding of the classifier’s behaviour. More... »

PAGES

585-600

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-28572-1_40

DOI

http://dx.doi.org/10.1007/978-3-642-28572-1_40

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053108866


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1013.3", 
          "name": [
            "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Douillard", 
        "givenName": "B.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1013.3", 
          "name": [
            "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Underwood", 
        "givenName": "J.", 
        "id": "sg:person.013552601745.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013552601745.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1013.3", 
          "name": [
            "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vlaskine", 
        "givenName": "V.", 
        "id": "sg:person.010526133115.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010526133115.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1013.3", 
          "name": [
            "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Quadros", 
        "givenName": "A.", 
        "id": "sg:person.011362260345.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011362260345.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1013.3", 
          "name": [
            "The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Singh", 
        "givenName": "S.", 
        "id": "sg:person.016506047247.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016506047247.09"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "This paper presents algorithms for fast segmentation of 3D point clouds and subsequent classification of the obtained 3D segments. The method jointly determines the ground surface and segments individual objects in 3D, including overhanging structures. When compared to six other terrain modelling techniques, this approach has minimal error between the sensed data and the representation; and is fast (processing a Velodyne scan in approximately 2 seconds). Applications include improved alignment of successive scans by enabling operations in sections (Velodyne scans are aligned 7% sharper compared to an approach using raw points) and more informed decision-making (paths move around overhangs). The use of segmentation to aid classification through 3D features, such as the Spin Image or the Spherical Harmonic Descriptor, is discussed and experimentally compared. Moreover, the segmentation facilitates a novel approach to 3D classification that bypasses feature extraction and directly compares 3D shapes via the ICP algorithm. This technique is shown to achieve accuracy on par with the best feature based classifier (92.1%) while being significantly faster and allowing a clearer understanding of the classifier\u2019s behaviour.", 
    "editor": [
      {
        "familyName": "Khatib", 
        "givenName": "Oussama", 
        "type": "Person"
      }, 
      {
        "familyName": "Kumar", 
        "givenName": "Vijay", 
        "type": "Person"
      }, 
      {
        "familyName": "Sukhatme", 
        "givenName": "Gaurav", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-28572-1_40", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-28571-4", 
        "978-3-642-28572-1"
      ], 
      "name": "Experimental Robotics", 
      "type": "Book"
    }, 
    "keywords": [
      "point clouds", 
      "use of segmentation", 
      "fast segmentation", 
      "classifier behavior", 
      "spin images", 
      "ICP algorithm", 
      "subsequent classification", 
      "segmentation", 
      "best features", 
      "individual objects", 
      "novel approach", 
      "minimal error", 
      "algorithm", 
      "cloud", 
      "classification", 
      "modelling techniques", 
      "spherical harmonic descriptors", 
      "classifier", 
      "improved alignment", 
      "ground surface", 
      "features", 
      "descriptors", 
      "images", 
      "objects", 
      "pipeline", 
      "technique", 
      "accuracy", 
      "representation", 
      "extraction", 
      "applications", 
      "error", 
      "operation", 
      "alignment", 
      "successive scans", 
      "data", 
      "par", 
      "method", 
      "behavior", 
      "surface", 
      "clear understanding", 
      "use", 
      "shape", 
      "structure", 
      "approach", 
      "segments", 
      "scans", 
      "sections", 
      "understanding", 
      "paper"
    ], 
    "name": "A Pipeline for the Segmentation and Classification of 3D Point Clouds", 
    "pagination": "585-600", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1053108866"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-28572-1_40"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-28572-1_40", 
      "https://app.dimensions.ai/details/publication/pub.1053108866"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-09-02T16:16", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/chapter/chapter_352.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-28572-1_40"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-28572-1_40'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-28572-1_40'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-28572-1_40'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-28572-1_40'


 

This table displays all metadata directly associated to this object as RDF triples.

145 TRIPLES      22 PREDICATES      74 URIs      67 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-28572-1_40 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N11094a56b3b54558876bcd7d13e313a4
4 schema:datePublished 2014
5 schema:datePublishedReg 2014-01-01
6 schema:description This paper presents algorithms for fast segmentation of 3D point clouds and subsequent classification of the obtained 3D segments. The method jointly determines the ground surface and segments individual objects in 3D, including overhanging structures. When compared to six other terrain modelling techniques, this approach has minimal error between the sensed data and the representation; and is fast (processing a Velodyne scan in approximately 2 seconds). Applications include improved alignment of successive scans by enabling operations in sections (Velodyne scans are aligned 7% sharper compared to an approach using raw points) and more informed decision-making (paths move around overhangs). The use of segmentation to aid classification through 3D features, such as the Spin Image or the Spherical Harmonic Descriptor, is discussed and experimentally compared. Moreover, the segmentation facilitates a novel approach to 3D classification that bypasses feature extraction and directly compares 3D shapes via the ICP algorithm. This technique is shown to achieve accuracy on par with the best feature based classifier (92.1%) while being significantly faster and allowing a clearer understanding of the classifier’s behaviour.
7 schema:editor N49574f6855ab4b8bb37f55f76dc226bc
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf N0875bbd55e3c497683cf3dfecf0c733a
11 schema:keywords ICP algorithm
12 accuracy
13 algorithm
14 alignment
15 applications
16 approach
17 behavior
18 best features
19 classification
20 classifier
21 classifier behavior
22 clear understanding
23 cloud
24 data
25 descriptors
26 error
27 extraction
28 fast segmentation
29 features
30 ground surface
31 images
32 improved alignment
33 individual objects
34 method
35 minimal error
36 modelling techniques
37 novel approach
38 objects
39 operation
40 paper
41 par
42 pipeline
43 point clouds
44 representation
45 scans
46 sections
47 segmentation
48 segments
49 shape
50 spherical harmonic descriptors
51 spin images
52 structure
53 subsequent classification
54 successive scans
55 surface
56 technique
57 understanding
58 use
59 use of segmentation
60 schema:name A Pipeline for the Segmentation and Classification of 3D Point Clouds
61 schema:pagination 585-600
62 schema:productId N56a608bca49a4ea6b2d295f5641be2bd
63 N905378a60e8e4a549286dd11cc81fee2
64 schema:publisher N11667b6cddd54b1ab7252a171acf1c68
65 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053108866
66 https://doi.org/10.1007/978-3-642-28572-1_40
67 schema:sdDatePublished 2022-09-02T16:16
68 schema:sdLicense https://scigraph.springernature.com/explorer/license/
69 schema:sdPublisher Ne1bd38a7d52342e4bdaeb03da8a620fe
70 schema:url https://doi.org/10.1007/978-3-642-28572-1_40
71 sgo:license sg:explorer/license/
72 sgo:sdDataset chapters
73 rdf:type schema:Chapter
74 N046ecf392f5d431a83baf194f83525d3 rdf:first N092f09aebd9c4678addfc92050c03968
75 rdf:rest Nf8863070a9d24b2ba240410d834501a7
76 N0875bbd55e3c497683cf3dfecf0c733a schema:isbn 978-3-642-28571-4
77 978-3-642-28572-1
78 schema:name Experimental Robotics
79 rdf:type schema:Book
80 N092f09aebd9c4678addfc92050c03968 schema:familyName Kumar
81 schema:givenName Vijay
82 rdf:type schema:Person
83 N11094a56b3b54558876bcd7d13e313a4 rdf:first N5719140614b941aba961709e5f943ecd
84 rdf:rest N491b3d5b3c6e411a8bb0bb50edb5f66d
85 N11667b6cddd54b1ab7252a171acf1c68 schema:name Springer Nature
86 rdf:type schema:Organisation
87 N1979532b1cce400fb414901a59c7cde5 rdf:first sg:person.010526133115.70
88 rdf:rest Nc0b58023ed30432b959e47e49a455db3
89 N1be936177321445bbf94d17c8026e9cf rdf:first sg:person.016506047247.09
90 rdf:rest rdf:nil
91 N491b3d5b3c6e411a8bb0bb50edb5f66d rdf:first sg:person.013552601745.79
92 rdf:rest N1979532b1cce400fb414901a59c7cde5
93 N49574f6855ab4b8bb37f55f76dc226bc rdf:first N7fe0bda5f70143ccb854bb21e0449040
94 rdf:rest N046ecf392f5d431a83baf194f83525d3
95 N56a608bca49a4ea6b2d295f5641be2bd schema:name doi
96 schema:value 10.1007/978-3-642-28572-1_40
97 rdf:type schema:PropertyValue
98 N5719140614b941aba961709e5f943ecd schema:affiliation grid-institutes:grid.1013.3
99 schema:familyName Douillard
100 schema:givenName B.
101 rdf:type schema:Person
102 N7fe0bda5f70143ccb854bb21e0449040 schema:familyName Khatib
103 schema:givenName Oussama
104 rdf:type schema:Person
105 N905378a60e8e4a549286dd11cc81fee2 schema:name dimensions_id
106 schema:value pub.1053108866
107 rdf:type schema:PropertyValue
108 Nc0b58023ed30432b959e47e49a455db3 rdf:first sg:person.011362260345.69
109 rdf:rest N1be936177321445bbf94d17c8026e9cf
110 Ncd5bc8d705644b63a5ad4bf3120a94f2 schema:familyName Sukhatme
111 schema:givenName Gaurav
112 rdf:type schema:Person
113 Ne1bd38a7d52342e4bdaeb03da8a620fe schema:name Springer Nature - SN SciGraph project
114 rdf:type schema:Organization
115 Nf8863070a9d24b2ba240410d834501a7 rdf:first Ncd5bc8d705644b63a5ad4bf3120a94f2
116 rdf:rest rdf:nil
117 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
118 schema:name Information and Computing Sciences
119 rdf:type schema:DefinedTerm
120 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
121 schema:name Artificial Intelligence and Image Processing
122 rdf:type schema:DefinedTerm
123 sg:person.010526133115.70 schema:affiliation grid-institutes:grid.1013.3
124 schema:familyName Vlaskine
125 schema:givenName V.
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010526133115.70
127 rdf:type schema:Person
128 sg:person.011362260345.69 schema:affiliation grid-institutes:grid.1013.3
129 schema:familyName Quadros
130 schema:givenName A.
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011362260345.69
132 rdf:type schema:Person
133 sg:person.013552601745.79 schema:affiliation grid-institutes:grid.1013.3
134 schema:familyName Underwood
135 schema:givenName J.
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013552601745.79
137 rdf:type schema:Person
138 sg:person.016506047247.09 schema:affiliation grid-institutes:grid.1013.3
139 schema:familyName Singh
140 schema:givenName S.
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016506047247.09
142 rdf:type schema:Person
143 grid-institutes:grid.1013.3 schema:alternateName The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia
144 schema:name The Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia
145 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...