Temporally Consistent Depth Estimation in Videos with Recurrent Architectures View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019-01-23

AUTHORS

Denis Tananaev , Huizhong Zhou , Benjamin Ummenhofer , Thomas Brox

ABSTRACT

Convolutional networks trained on large RGB-D datasets have enabled depth estimation from a single image. Many works on automotive applications rely on such approaches. However, all existing methods work on a frame-by-frame manner when applied to videos, which leads to inconsistent depth estimates over time. In this paper, we introduce for the first time an approach that yields temporally consistent depth estimates over multiple frames of a video. This is done by a dedicated architecture based on convolutional LSTM units and layer normalization. Our approach achieves superior performance on several error metrics when compared to independent frame processing. This also shows in an improved quality of the reconstructed multi-view point clouds. More... »

PAGES

689-701

Book

TITLE

Computer Vision – ECCV 2018 Workshops

ISBN

978-3-030-11014-7
978-3-030-11015-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-11015-4_52

DOI

http://dx.doi.org/10.1007/978-3-030-11015-4_52

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111703398


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": "Robert Bosch GmbH, Stuttgart, Germany", 
          "id": "http://www.grid.ac/institutes/grid.6584.f", 
          "name": [
            "University of Freiburg, Freiburg im Breisgau, Germany", 
            "Robert Bosch GmbH, Stuttgart, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tananaev", 
        "givenName": "Denis", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Freiburg, Freiburg im Breisgau, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg im Breisgau, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhou", 
        "givenName": "Huizhong", 
        "id": "sg:person.015337214304.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015337214304.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Freiburg, Freiburg im Breisgau, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg im Breisgau, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ummenhofer", 
        "givenName": "Benjamin", 
        "id": "sg:person.010435022672.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010435022672.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Freiburg, Freiburg im Breisgau, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg im Breisgau, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brox", 
        "givenName": "Thomas", 
        "id": "sg:person.012443225372.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2019-01-23", 
    "datePublishedReg": "2019-01-23", 
    "description": "Convolutional networks trained on large RGB-D datasets have enabled depth estimation from a single image. Many works on automotive applications rely on such approaches. However, all existing methods work on a frame-by-frame manner when applied to videos, which leads to inconsistent depth estimates over time. In this paper, we introduce for the first time an approach that yields temporally consistent depth estimates over multiple frames of a video. This is done by a dedicated architecture based on convolutional LSTM units and layer normalization. Our approach achieves superior performance on several error metrics when compared to independent frame processing. This also shows in an improved quality of the reconstructed multi-view point clouds.", 
    "editor": [
      {
        "familyName": "Leal-Taix\u00e9", 
        "givenName": "Laura", 
        "type": "Person"
      }, 
      {
        "familyName": "Roth", 
        "givenName": "Stefan", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-11015-4_52", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-030-11014-7", 
        "978-3-030-11015-4"
      ], 
      "name": "Computer Vision \u2013 ECCV 2018 Workshops", 
      "type": "Book"
    }, 
    "keywords": [
      "depth estimation", 
      "large RGB-D dataset", 
      "consistent depth estimation", 
      "RGB-D dataset", 
      "multi-view point clouds", 
      "convolutional network", 
      "dedicated architecture", 
      "layer normalization", 
      "recurrent architecture", 
      "LSTM units", 
      "single image", 
      "frame manner", 
      "frame processing", 
      "multiple frames", 
      "point clouds", 
      "error metrics", 
      "video", 
      "superior performance", 
      "automotive applications", 
      "architecture", 
      "such approaches", 
      "depth estimates", 
      "dataset", 
      "cloud", 
      "network", 
      "consistent depth", 
      "frame", 
      "metrics", 
      "images", 
      "estimation", 
      "processing", 
      "applications", 
      "performance", 
      "improved quality", 
      "time", 
      "quality", 
      "work", 
      "first time", 
      "depth", 
      "method", 
      "approach", 
      "normalization", 
      "manner", 
      "units", 
      "estimates", 
      "paper"
    ], 
    "name": "Temporally Consistent Depth Estimation in Videos with Recurrent Architectures", 
    "pagination": "689-701", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111703398"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-11015-4_52"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-11015-4_52", 
      "https://app.dimensions.ai/details/publication/pub.1111703398"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-11-24T21:12", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/chapter/chapter_177.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-11015-4_52"
  }
]
 

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.10.1007/978-3-030-11015-4_52'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-11015-4_52'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-11015-4_52'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-11015-4_52'


 

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

134 TRIPLES      22 PREDICATES      70 URIs      63 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-11015-4_52 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nd25bc62a2b5c4ce9b5421cb0a6566e40
4 schema:datePublished 2019-01-23
5 schema:datePublishedReg 2019-01-23
6 schema:description Convolutional networks trained on large RGB-D datasets have enabled depth estimation from a single image. Many works on automotive applications rely on such approaches. However, all existing methods work on a frame-by-frame manner when applied to videos, which leads to inconsistent depth estimates over time. In this paper, we introduce for the first time an approach that yields temporally consistent depth estimates over multiple frames of a video. This is done by a dedicated architecture based on convolutional LSTM units and layer normalization. Our approach achieves superior performance on several error metrics when compared to independent frame processing. This also shows in an improved quality of the reconstructed multi-view point clouds.
7 schema:editor N73acfa2a9b1343eea237d12bdf062a3c
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf N7e7c05efa21e4b7bbc4cb74f7d8778ff
11 schema:keywords LSTM units
12 RGB-D dataset
13 applications
14 approach
15 architecture
16 automotive applications
17 cloud
18 consistent depth
19 consistent depth estimation
20 convolutional network
21 dataset
22 dedicated architecture
23 depth
24 depth estimates
25 depth estimation
26 error metrics
27 estimates
28 estimation
29 first time
30 frame
31 frame manner
32 frame processing
33 images
34 improved quality
35 large RGB-D dataset
36 layer normalization
37 manner
38 method
39 metrics
40 multi-view point clouds
41 multiple frames
42 network
43 normalization
44 paper
45 performance
46 point clouds
47 processing
48 quality
49 recurrent architecture
50 single image
51 such approaches
52 superior performance
53 time
54 units
55 video
56 work
57 schema:name Temporally Consistent Depth Estimation in Videos with Recurrent Architectures
58 schema:pagination 689-701
59 schema:productId N0562ff9867e84b358e0daf0bb2c499b7
60 Ncb2fede629a84d12aa42f4c53ded3aef
61 schema:publisher Nd142ecf9702a46c186931db8b09f4301
62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111703398
63 https://doi.org/10.1007/978-3-030-11015-4_52
64 schema:sdDatePublished 2022-11-24T21:12
65 schema:sdLicense https://scigraph.springernature.com/explorer/license/
66 schema:sdPublisher N0c190c8a259c4f1c89c4c6933270427b
67 schema:url https://doi.org/10.1007/978-3-030-11015-4_52
68 sgo:license sg:explorer/license/
69 sgo:sdDataset chapters
70 rdf:type schema:Chapter
71 N0562ff9867e84b358e0daf0bb2c499b7 schema:name doi
72 schema:value 10.1007/978-3-030-11015-4_52
73 rdf:type schema:PropertyValue
74 N0c190c8a259c4f1c89c4c6933270427b schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 N42cb2dd834244268a4513e7d63dff567 rdf:first sg:person.015337214304.05
77 rdf:rest N929f6d2310554ac8b17e52b4c6689b86
78 N50c8fbb5f0e4478085abdc5a61e1eeef schema:affiliation grid-institutes:grid.6584.f
79 schema:familyName Tananaev
80 schema:givenName Denis
81 rdf:type schema:Person
82 N6639c1ea3a28402aaf376906ac0fe61d rdf:first sg:person.012443225372.65
83 rdf:rest rdf:nil
84 N73acfa2a9b1343eea237d12bdf062a3c rdf:first Nf2fb3d87497643419ed56bbd514e2001
85 rdf:rest Nd8d262aafb1342218832dfc7143d93b1
86 N7e7c05efa21e4b7bbc4cb74f7d8778ff schema:isbn 978-3-030-11014-7
87 978-3-030-11015-4
88 schema:name Computer Vision – ECCV 2018 Workshops
89 rdf:type schema:Book
90 N929f6d2310554ac8b17e52b4c6689b86 rdf:first sg:person.010435022672.00
91 rdf:rest N6639c1ea3a28402aaf376906ac0fe61d
92 Nae3dd8a574264b5db547027434322c62 schema:familyName Roth
93 schema:givenName Stefan
94 rdf:type schema:Person
95 Ncb2fede629a84d12aa42f4c53ded3aef schema:name dimensions_id
96 schema:value pub.1111703398
97 rdf:type schema:PropertyValue
98 Nd142ecf9702a46c186931db8b09f4301 schema:name Springer Nature
99 rdf:type schema:Organisation
100 Nd25bc62a2b5c4ce9b5421cb0a6566e40 rdf:first N50c8fbb5f0e4478085abdc5a61e1eeef
101 rdf:rest N42cb2dd834244268a4513e7d63dff567
102 Nd8d262aafb1342218832dfc7143d93b1 rdf:first Nae3dd8a574264b5db547027434322c62
103 rdf:rest rdf:nil
104 Nf2fb3d87497643419ed56bbd514e2001 schema:familyName Leal-Taixé
105 schema:givenName Laura
106 rdf:type schema:Person
107 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
108 schema:name Information and Computing Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
111 schema:name Artificial Intelligence and Image Processing
112 rdf:type schema:DefinedTerm
113 sg:person.010435022672.00 schema:affiliation grid-institutes:grid.5963.9
114 schema:familyName Ummenhofer
115 schema:givenName Benjamin
116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010435022672.00
117 rdf:type schema:Person
118 sg:person.012443225372.65 schema:affiliation grid-institutes:grid.5963.9
119 schema:familyName Brox
120 schema:givenName Thomas
121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65
122 rdf:type schema:Person
123 sg:person.015337214304.05 schema:affiliation grid-institutes:grid.5963.9
124 schema:familyName Zhou
125 schema:givenName Huizhong
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015337214304.05
127 rdf:type schema:Person
128 grid-institutes:grid.5963.9 schema:alternateName University of Freiburg, Freiburg im Breisgau, Germany
129 schema:name University of Freiburg, Freiburg im Breisgau, Germany
130 rdf:type schema:Organization
131 grid-institutes:grid.6584.f schema:alternateName Robert Bosch GmbH, Stuttgart, Germany
132 schema:name Robert Bosch GmbH, Stuttgart, Germany
133 University of Freiburg, Freiburg im Breisgau, Germany
134 rdf:type schema:Organization
 




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


...