Self-supervised 3D keypoint learning for monocular visual odometry

A method for learning depth-aware keypoints and associated descriptors from monocular video for monocular visual odometry is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Pillai, Sudeep, Guizilini, Vitor, Ambrus, Rares A, Tang, Jiexiong, Gaidon, Adrien David, Kim, Hanme
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Pillai, Sudeep
Guizilini, Vitor
Ambrus, Rares A
Tang, Jiexiong
Gaidon, Adrien David
Kim, Hanme
description A method for learning depth-aware keypoints and associated descriptors from monocular video for monocular visual odometry is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target image and a context image from successive images of the monocular video. The method also includes lifting 2D keypoints from the target image to learn 3D keypoints based on a learned depth map from the depth network. The method further includes estimating a trajectory of an ego-vehicle based on the learned 3D keypoints.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US12073580B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US12073580B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US12073580B23</originalsourceid><addsrcrecordid>eNrjZHAKTs1J0y0uLUgtKsssTk1RMHZRyE6tLMjPzCtRyElNLMrLzEtXSMsvUsjNz8tPLs1JLFIAKixNzFHIT8nPTS0pquRhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfGhwYZGBubGphYGTkbGxKgBAGFIMoo</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Self-supervised 3D keypoint learning for monocular visual odometry</title><source>esp@cenet</source><creator>Pillai, Sudeep ; Guizilini, Vitor ; Ambrus, Rares A ; Tang, Jiexiong ; Gaidon, Adrien David ; Kim, Hanme</creator><creatorcontrib>Pillai, Sudeep ; Guizilini, Vitor ; Ambrus, Rares A ; Tang, Jiexiong ; Gaidon, Adrien David ; Kim, Hanme</creatorcontrib><description>A method for learning depth-aware keypoints and associated descriptors from monocular video for monocular visual odometry is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target image and a context image from successive images of the monocular video. The method also includes lifting 2D keypoints from the target image to learn 3D keypoints based on a learned depth map from the depth network. The method further includes estimating a trajectory of an ego-vehicle based on the learned 3D keypoints.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION ; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PERFORMING OPERATIONS ; PHYSICS ; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT ; TRANSPORTING ; VEHICLES IN GENERAL</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240827&amp;DB=EPODOC&amp;CC=US&amp;NR=12073580B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240827&amp;DB=EPODOC&amp;CC=US&amp;NR=12073580B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Pillai, Sudeep</creatorcontrib><creatorcontrib>Guizilini, Vitor</creatorcontrib><creatorcontrib>Ambrus, Rares A</creatorcontrib><creatorcontrib>Tang, Jiexiong</creatorcontrib><creatorcontrib>Gaidon, Adrien David</creatorcontrib><creatorcontrib>Kim, Hanme</creatorcontrib><title>Self-supervised 3D keypoint learning for monocular visual odometry</title><description>A method for learning depth-aware keypoints and associated descriptors from monocular video for monocular visual odometry is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target image and a context image from successive images of the monocular video. The method also includes lifting 2D keypoints from the target image to learn 3D keypoints based on a learned depth map from the depth network. The method further includes estimating a trajectory of an ego-vehicle based on the learned 3D keypoints.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION</subject><subject>CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PERFORMING OPERATIONS</subject><subject>PHYSICS</subject><subject>ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT</subject><subject>TRANSPORTING</subject><subject>VEHICLES IN GENERAL</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHAKTs1J0y0uLUgtKsssTk1RMHZRyE6tLMjPzCtRyElNLMrLzEtXSMsvUsjNz8tPLs1JLFIAKixNzFHIT8nPTS0pquRhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfGhwYZGBubGphYGTkbGxKgBAGFIMoo</recordid><startdate>20240827</startdate><enddate>20240827</enddate><creator>Pillai, Sudeep</creator><creator>Guizilini, Vitor</creator><creator>Ambrus, Rares A</creator><creator>Tang, Jiexiong</creator><creator>Gaidon, Adrien David</creator><creator>Kim, Hanme</creator><scope>EVB</scope></search><sort><creationdate>20240827</creationdate><title>Self-supervised 3D keypoint learning for monocular visual odometry</title><author>Pillai, Sudeep ; Guizilini, Vitor ; Ambrus, Rares A ; Tang, Jiexiong ; Gaidon, Adrien David ; Kim, Hanme</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US12073580B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION</topic><topic>CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PERFORMING OPERATIONS</topic><topic>PHYSICS</topic><topic>ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT</topic><topic>TRANSPORTING</topic><topic>VEHICLES IN GENERAL</topic><toplevel>online_resources</toplevel><creatorcontrib>Pillai, Sudeep</creatorcontrib><creatorcontrib>Guizilini, Vitor</creatorcontrib><creatorcontrib>Ambrus, Rares A</creatorcontrib><creatorcontrib>Tang, Jiexiong</creatorcontrib><creatorcontrib>Gaidon, Adrien David</creatorcontrib><creatorcontrib>Kim, Hanme</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pillai, Sudeep</au><au>Guizilini, Vitor</au><au>Ambrus, Rares A</au><au>Tang, Jiexiong</au><au>Gaidon, Adrien David</au><au>Kim, Hanme</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Self-supervised 3D keypoint learning for monocular visual odometry</title><date>2024-08-27</date><risdate>2024</risdate><abstract>A method for learning depth-aware keypoints and associated descriptors from monocular video for monocular visual odometry is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target image and a context image from successive images of the monocular video. The method also includes lifting 2D keypoints from the target image to learn 3D keypoints based on a learned depth map from the depth network. The method further includes estimating a trajectory of an ego-vehicle based on the learned 3D keypoints.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US12073580B2
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION
CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PERFORMING OPERATIONS
PHYSICS
ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT
TRANSPORTING
VEHICLES IN GENERAL
title Self-supervised 3D keypoint learning for monocular visual odometry
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T14%3A12%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Pillai,%20Sudeep&rft.date=2024-08-27&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS12073580B2%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true