DeepTAM: Deep Tracking and Mapping View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2018-10-06

AUTHORS

Huizhong Zhou , Benjamin Ummenhofer , Thomas Brox

ABSTRACT

We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motions. Further, we show that generating a large number of pose hypotheses leads to more accurate predictions. For mapping, we accumulate information in a cost volume centered at the current depth estimate. The mapping network then combines the cost volume and the keyframe image to update the depth prediction, thereby effectively making use of depth measurements and image-based priors. Our approach yields state-of-the-art results with few images and is robust with respect to noisy camera poses. We demonstrate that the performance of our 6 DOF tracking competes with RGB-D tracking algorithms.We compare favorably against strong classic and deep learning powered dense depth algorithms. More... »

PAGES

851-868

Book

TITLE

Computer Vision – ECCV 2018

ISBN

978-3-030-01269-4
978-3-030-01270-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-01270-0_50

DOI

http://dx.doi.org/10.1007/978-3-030-01270-0_50

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107454859


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": "University of Freiburg, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, 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, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, 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, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, 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": "2018-10-06", 
    "datePublishedReg": "2018-10-06", 
    "description": "We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motions. Further, we show that generating a large number of pose hypotheses leads to more accurate predictions. For mapping, we accumulate information in a cost volume centered at the current depth estimate. The mapping network then combines the cost volume and the keyframe image to update the depth prediction, thereby effectively making use of depth measurements and image-based priors. Our approach yields state-of-the-art results with few images and is robust with respect to noisy camera poses. We demonstrate that the performance of our 6\u00a0DOF tracking competes with RGB-D tracking algorithms.We compare favorably against strong classic and deep learning powered dense depth algorithms.", 
    "editor": [
      {
        "familyName": "Ferrari", 
        "givenName": "Vittorio", 
        "type": "Person"
      }, 
      {
        "familyName": "Hebert", 
        "givenName": "Martial", 
        "type": "Person"
      }, 
      {
        "familyName": "Sminchisescu", 
        "givenName": "Cristian", 
        "type": "Person"
      }, 
      {
        "familyName": "Weiss", 
        "givenName": "Yair", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-01270-0_50", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-030-01269-4", 
        "978-3-030-01270-0"
      ], 
      "name": "Computer Vision \u2013 ECCV 2018", 
      "type": "Book"
    }, 
    "keywords": [
      "cost volume", 
      "current camera image", 
      "depth map estimation", 
      "image-based priors", 
      "current depth estimate", 
      "camera tracking", 
      "camera pose", 
      "deep learning", 
      "pose hypotheses", 
      "camera motion", 
      "keyframe images", 
      "dataset bias", 
      "Deep Tracking", 
      "art results", 
      "camera images", 
      "learning problem", 
      "mapping network", 
      "depth prediction", 
      "tracking algorithm", 
      "map estimation", 
      "more accurate predictions", 
      "tracking", 
      "synthetic viewpoint", 
      "algorithm", 
      "images", 
      "depth estimates", 
      "depth algorithm", 
      "accurate prediction", 
      "large number", 
      "depth measurements", 
      "pose", 
      "network", 
      "learning", 
      "mapping", 
      "priors", 
      "information", 
      "prediction", 
      "performance", 
      "system", 
      "viewpoint", 
      "estimation", 
      "motion", 
      "number", 
      "use", 
      "measurements", 
      "volume", 
      "state", 
      "results", 
      "increment", 
      "respect", 
      "problem", 
      "approach", 
      "competes", 
      "estimates", 
      "bias", 
      "hypothesis"
    ], 
    "name": "DeepTAM: Deep Tracking and Mapping", 
    "pagination": "851-868", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1107454859"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-01270-0_50"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-01270-0_50", 
      "https://app.dimensions.ai/details/publication/pub.1107454859"
    ], 
    "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_39.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-01270-0_50"
  }
]
 

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.10.1007/978-3-030-01270-0_50'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-01270-0_50'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-01270-0_50'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-01270-0_50'


 

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

144 TRIPLES      22 PREDICATES      80 URIs      73 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-01270-0_50 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N6e2c22050b3042a68b6774e00e2cc196
4 schema:datePublished 2018-10-06
5 schema:datePublishedReg 2018-10-06
6 schema:description We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motions. Further, we show that generating a large number of pose hypotheses leads to more accurate predictions. For mapping, we accumulate information in a cost volume centered at the current depth estimate. The mapping network then combines the cost volume and the keyframe image to update the depth prediction, thereby effectively making use of depth measurements and image-based priors. Our approach yields state-of-the-art results with few images and is robust with respect to noisy camera poses. We demonstrate that the performance of our 6 DOF tracking competes with RGB-D tracking algorithms.We compare favorably against strong classic and deep learning powered dense depth algorithms.
7 schema:editor N83f0e3ae4f94459ca9a984dff9b8da5f
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf N0b432e3b28334499b83f664afd758c8c
11 schema:keywords Deep Tracking
12 accurate prediction
13 algorithm
14 approach
15 art results
16 bias
17 camera images
18 camera motion
19 camera pose
20 camera tracking
21 competes
22 cost volume
23 current camera image
24 current depth estimate
25 dataset bias
26 deep learning
27 depth algorithm
28 depth estimates
29 depth map estimation
30 depth measurements
31 depth prediction
32 estimates
33 estimation
34 hypothesis
35 image-based priors
36 images
37 increment
38 information
39 keyframe images
40 large number
41 learning
42 learning problem
43 map estimation
44 mapping
45 mapping network
46 measurements
47 more accurate predictions
48 motion
49 network
50 number
51 performance
52 pose
53 pose hypotheses
54 prediction
55 priors
56 problem
57 respect
58 results
59 state
60 synthetic viewpoint
61 system
62 tracking
63 tracking algorithm
64 use
65 viewpoint
66 volume
67 schema:name DeepTAM: Deep Tracking and Mapping
68 schema:pagination 851-868
69 schema:productId N417c275df4e246ceaab0e7865155f3e2
70 N89db18dcda8544c1b99173dcc38d90d5
71 schema:publisher Nda6360afe80a475db765116f3ae22257
72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107454859
73 https://doi.org/10.1007/978-3-030-01270-0_50
74 schema:sdDatePublished 2022-09-02T16:16
75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
76 schema:sdPublisher Nbaed99b37c074693b12914878ee11674
77 schema:url https://doi.org/10.1007/978-3-030-01270-0_50
78 sgo:license sg:explorer/license/
79 sgo:sdDataset chapters
80 rdf:type schema:Chapter
81 N0b432e3b28334499b83f664afd758c8c schema:isbn 978-3-030-01269-4
82 978-3-030-01270-0
83 schema:name Computer Vision – ECCV 2018
84 rdf:type schema:Book
85 N1a314949bae64dc5b63004ef9748cb67 rdf:first N7228e1a6537b4efd848f6d7d8e2cf231
86 rdf:rest rdf:nil
87 N417c275df4e246ceaab0e7865155f3e2 schema:name dimensions_id
88 schema:value pub.1107454859
89 rdf:type schema:PropertyValue
90 N482e9bd446d146f898e13de158f804d4 rdf:first sg:person.012443225372.65
91 rdf:rest rdf:nil
92 N634c48b384b446008cb2f76cd6df5e43 rdf:first Nd6b4f0903437483cb36e9e7f2ac47e15
93 rdf:rest N1a314949bae64dc5b63004ef9748cb67
94 N6e2c22050b3042a68b6774e00e2cc196 rdf:first sg:person.015337214304.05
95 rdf:rest Nc0a7c38d410c4ed49727f02737130dbb
96 N7228e1a6537b4efd848f6d7d8e2cf231 schema:familyName Weiss
97 schema:givenName Yair
98 rdf:type schema:Person
99 N83f0e3ae4f94459ca9a984dff9b8da5f rdf:first Nae61203fb49740c596acb2b67e7f9e19
100 rdf:rest Nf586617a21644fdc9b0827c87505c483
101 N89db18dcda8544c1b99173dcc38d90d5 schema:name doi
102 schema:value 10.1007/978-3-030-01270-0_50
103 rdf:type schema:PropertyValue
104 Nae61203fb49740c596acb2b67e7f9e19 schema:familyName Ferrari
105 schema:givenName Vittorio
106 rdf:type schema:Person
107 Nbaed99b37c074693b12914878ee11674 schema:name Springer Nature - SN SciGraph project
108 rdf:type schema:Organization
109 Nc0a7c38d410c4ed49727f02737130dbb rdf:first sg:person.010435022672.00
110 rdf:rest N482e9bd446d146f898e13de158f804d4
111 Ncb4b062d26b64466bf4e1748ef8b01d1 schema:familyName Hebert
112 schema:givenName Martial
113 rdf:type schema:Person
114 Nd6b4f0903437483cb36e9e7f2ac47e15 schema:familyName Sminchisescu
115 schema:givenName Cristian
116 rdf:type schema:Person
117 Nda6360afe80a475db765116f3ae22257 schema:name Springer Nature
118 rdf:type schema:Organisation
119 Nf586617a21644fdc9b0827c87505c483 rdf:first Ncb4b062d26b64466bf4e1748ef8b01d1
120 rdf:rest N634c48b384b446008cb2f76cd6df5e43
121 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
122 schema:name Information and Computing Sciences
123 rdf:type schema:DefinedTerm
124 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
125 schema:name Artificial Intelligence and Image Processing
126 rdf:type schema:DefinedTerm
127 sg:person.010435022672.00 schema:affiliation grid-institutes:grid.5963.9
128 schema:familyName Ummenhofer
129 schema:givenName Benjamin
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010435022672.00
131 rdf:type schema:Person
132 sg:person.012443225372.65 schema:affiliation grid-institutes:grid.5963.9
133 schema:familyName Brox
134 schema:givenName Thomas
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65
136 rdf:type schema:Person
137 sg:person.015337214304.05 schema:affiliation grid-institutes:grid.5963.9
138 schema:familyName Zhou
139 schema:givenName Huizhong
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015337214304.05
141 rdf:type schema:Person
142 grid-institutes:grid.5963.9 schema:alternateName University of Freiburg, Freiburg, Germany
143 schema:name University of Freiburg, Freiburg, Germany
144 rdf:type schema:Organization
 




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


...