Low-Cost Depth Camera Pose Tracking for Mobile Platforms
The KinectFusion algorithm is now used routinely to reconstruct dense 3D surfaces at real-time frame rates using a commodity depth camera. To achieve robust pose estimation, the method conducts the frame-to-model tracking during camera tracking that must inevitably accompany the memory-bound, GPU-as...
Gespeichert in:
| Veröffentlicht in: | 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) S. 123 - 126 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.09.2016
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The KinectFusion algorithm is now used routinely to reconstruct dense 3D surfaces at real-time frame rates using a commodity depth camera. To achieve robust pose estimation, the method conducts the frame-to-model tracking during camera tracking that must inevitably accompany the memory-bound, GPU-assisted volumetric computations for the model manipulation, to which mobile processors are often more vulnerable than PC-based processors. In this paper, we present an effective camera-tracking method that is based on the computationally lighter frame-to-frame tracking method. This method's tendency toward rapid accumulation of pose estimation errors is suppressed effectively via a predictor-corrector technique. By removing the costly volumetric computations from the pose estimation process, our camera tracking system becomes more efficient in terms of both time and space complexity, offering a compact implementation of depth sensor-based camera tracking on low-end platforms such as mobile devices in addition to high-end PCs. |
|---|---|
| AbstractList | The KinectFusion algorithm is now used routinely to reconstruct dense 3D surfaces at real-time frame rates using a commodity depth camera. To achieve robust pose estimation, the method conducts the frame-to-model tracking during camera tracking that must inevitably accompany the memory-bound, GPU-assisted volumetric computations for the model manipulation, to which mobile processors are often more vulnerable than PC-based processors. In this paper, we present an effective camera-tracking method that is based on the computationally lighter frame-to-frame tracking method. This method's tendency toward rapid accumulation of pose estimation errors is suppressed effectively via a predictor-corrector technique. By removing the costly volumetric computations from the pose estimation process, our camera tracking system becomes more efficient in terms of both time and space complexity, offering a compact implementation of depth sensor-based camera tracking on low-end platforms such as mobile devices in addition to high-end PCs. |
| Author | Ingu Park Jiman Jeong Jaehyun Lee Youngwook Kim Insung Ihm |
| Author_xml | – sequence: 1 surname: Insung Ihm fullname: Insung Ihm email: ihm@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 2 surname: Youngwook Kim fullname: Youngwook Kim email: kimyu7@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 3 surname: Jaehyun Lee fullname: Jaehyun Lee email: kidsnow@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 4 surname: Jiman Jeong fullname: Jiman Jeong email: sixzone11@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 5 surname: Ingu Park fullname: Ingu Park email: ssault@ncsoft.com organization: NCSOFT Corp., South Korea |
| BookMark | eNotjctOwzAUBY0EC1r4Ahb4Bxyu7cSul1F4tFIqKsi-sp1rCCRxlRgh_p5IsDmj2cxZkfMxjkjILYeMczB3u9d9-cLK9uNr9CkTwFUGUOgzsuIFGJA6B3lJNnX8ZlWcE73HU3qnlR1wsvQQZ6TNZP1nN77RECe6j67rkR56mxYd5ityEWw_4_U_16R5fGiqLaufn3ZVWbPOQGIOBIDhCKIFzLVebpXmEmRAFK0Gq50JQSjjg9CtU8Jri9aKkAffFs7LNbn5y3aIeDxN3WCnn6PeSJUv8ws_VUUP |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ISMAR-Adjunct.2016.0057 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore Digital Libary (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 1509037403 9781509037407 |
| EndPage | 126 |
| ExternalDocumentID | 7836478 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i90t-b020091e02d0e477037671303fee2d70a7b9ff269cf27db62c7aeaa2f4fcd5bc3 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jun 29 18:37:36 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i90t-b020091e02d0e477037671303fee2d70a7b9ff269cf27db62c7aeaa2f4fcd5bc3 |
| PageCount | 4 |
| ParticipantIDs | ieee_primary_7836478 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-Sept. |
| PublicationDateYYYYMMDD | 2016-09-01 |
| PublicationDate_xml | – month: 09 year: 2016 text: 2016-Sept. |
| PublicationDecade | 2010 |
| PublicationTitle | 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) |
| PublicationTitleAbbrev | ISMARW |
| PublicationYear | 2016 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.6263349 |
| Snippet | The KinectFusion algorithm is now used routinely to reconstruct dense 3D surfaces at real-time frame rates using a commodity depth camera. To achieve robust... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 123 |
| SubjectTerms | Cameras Computational modeling I.3.3 [Computer Graphics]: Picture/Image Generation-Digitizing and Scanning; I.4.8 [Image Processing and Computer Vision]: Scene Analysis-Tracking H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems-Artificial Iterative closest point algorithm Mobile communication Pose estimation Solid modeling Three-dimensional displays |
| Title | Low-Cost Depth Camera Pose Tracking for Mobile Platforms |
| URI | https://ieeexplore.ieee.org/document/7836478 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3BSgMxEB3a4sGTSitqVXLwaGya3Ww2x1ItCrYs2kNvJclOsCLd0t3q75tsS0Xw4i2EQJiEyRtm3ssA3GCUWKEiQVEoTv3rp2kaWUel0lxZZxLNTN1sQk4m6Wymsgbc7rUwiFiTz_AuDOtafl7YTUiV9YLiIJZpE5pSJlut1o6y1Weq9_Q6HrzQQf7uASGwJPuhzCB-t02pUWN09L_9jqHzI78j2R5YTqCByzakz8UXHRZlRe5xVb2RoQ7pJJIVJRIPODakvImPQMm4MN7TSfahqxCQlh2Yjh6mw0e6a3tAF4pV1LBQsOgj4znDWHqPlIkMSOMQeS6ZlkY5xxN_klzmJuFWatSau9jZXBgbnUJrWSzxDAg6oQO_gWmnY2l1mvo1SsU-5nBxIsw5tIPR89X2Y4v5zt6Lv6e7cBgOdUuwuoRWtd7gFRzYz2pRrq_r2_gGWa6N_Q |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA21CnpSacVvc_BobJpNNptjqZYW27JoD72VJDtBRbqlu9W_b7ItFcGLtxACYRImb5h5L4PQLUSxFSoSBIRixL9-miSRdUQqzZR1JtbUVM0m5HicTKcqraG7rRYGACryGdyHYVXLz3K7CqmyVlAccJnsoF3BOaNrtdaGtNWmqjV4GXWeSSd795AQeJLtUGgQvxunVLjRO_zfjkeo-SPAw-kWWo5RDeYNlAzzL9LNixI_wKJ8xV0dEko4zQvAHnJsSHpjH4PiUW68r-P0Q5chJC2aaNJ7nHT7ZNP4gLwpWhJDQ8miDZRlFLj0PiljGbDGAbBMUi2Nco7F_iyZzEzMrNSgNXPc2UwYG52g-jyfwynC4IQODAeqnebS6iTxa5TiPupwPBbmDDWC0bPF-muL2cbe87-nb9B-fzIazoaD8dMFOggHvKZbXaJ6uVzBFdqzn-VbsbyubuYb7K-RRA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2016+IEEE+International+Symposium+on+Mixed+and+Augmented+Reality+%28ISMAR-Adjunct%29&rft.atitle=Low-Cost+Depth+Camera+Pose+Tracking+for+Mobile+Platforms&rft.au=Insung+Ihm&rft.au=Youngwook+Kim&rft.au=Jaehyun+Lee&rft.au=Jiman+Jeong&rft.date=2016-09-01&rft.pub=IEEE&rft.spage=123&rft.epage=126&rft_id=info:doi/10.1109%2FISMAR-Adjunct.2016.0057&rft.externalDocID=7836478 |