PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural...
Uložené v:
| Vydané v: | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 77 - 85 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.07.2017
|
| Predmet: | |
| ISSN: | 1063-6919, 1063-6919 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption. |
|---|---|
| AbstractList | Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption. |
| Author | Charles, R. Qi Hao Su Mo Kaichun Guibas, Leonidas J. |
| Author_xml | – sequence: 1 givenname: R. Qi surname: Charles fullname: Charles, R. Qi organization: Stanford Univ., Stanford, CA, USA – sequence: 2 surname: Hao Su fullname: Hao Su organization: Stanford Univ., Stanford, CA, USA – sequence: 3 surname: Mo Kaichun fullname: Mo Kaichun organization: Stanford Univ., Stanford, CA, USA – sequence: 4 givenname: Leonidas J. surname: Guibas fullname: Guibas, Leonidas J. organization: Stanford Univ., Stanford, CA, USA |
| BookMark | eNpNjMtOwzAURA0qEm1hx46NfyDhXtvxgx1KeUkRVLy2lXGuK6PWqZJs-HuqwoLVHM0ZzYxNcpeJsQuEEhHcVf2xfCkFoClRH7EZVtJqUJVRx2yKoGWhHbrJPz5ls2H4AhDSCJiyZtmlPD7ReM0XRDvekO9zymveZX5Q_JXGgceu53LB640fhhRT8GPaD3xu93q9pTweijN2Ev1moPO_nLP3u9u3-qFonu8f65umSELhWEShjHWfaEnYEJST2ovQmhZcRIhSkDQBbMTWChQymAjatMYpLysZNEk5Z5e_v4mIVrs-bX3_vbLgnHJO_gBw8E5o |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/CVPR.2017.16 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Computer Science |
| EISBN | 1538604574 9781538604571 |
| EISSN | 1063-6919 |
| EndPage | 85 |
| ExternalDocumentID | 8099499 |
| Genre | orig-research |
| GroupedDBID | 23M 29F 29O 6IE 6IH 6IK ABDPE ACGFS ALMA_UNASSIGNED_HOLDINGS CBEJK IPLJI M43 RIE RIO RNS |
| ID | FETCH-LOGICAL-i241t-f24789b18e28cc4936a2cd7d09f10f32e37c08f1d82123c7f067d794a353c6e33 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 4776 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000418371400009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1063-6919 |
| IngestDate | Wed Aug 27 02:33:38 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i241t-f24789b18e28cc4936a2cd7d09f10f32e37c08f1d82123c7f067d794a353c6e33 |
| PageCount | 9 |
| ParticipantIDs | ieee_primary_8099499 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-07 |
| PublicationDateYYYYMMDD | 2017-07-01 |
| PublicationDate_xml | – month: 07 year: 2017 text: 2017-07 |
| PublicationDecade | 2010 |
| PublicationTitle | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
| PublicationTitleAbbrev | CVPR |
| PublicationYear | 2017 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0023720 ssj0003211698 |
| Score | 2.6226795 |
| Snippet | Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 77 |
| SubjectTerms | Computer architecture Feature extraction Machine learning Semantics Shape Three-dimensional displays |
| Title | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation |
| URI | https://ieeexplore.ieee.org/document/8099499 |
| WOSCitedRecordID | wos000418371400009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELbaioGpQIt4ywMjoYmdxDZrS8WAqoiXulWpfa46kFRtyu_n7KZBSCxs0UWKIp9P3-d7-CPk1qhEzDVuXsWMCWKZGoy50J15gDEdyjhmxotNiMlETqcqa5G7ZhYGAHzzGdy7R1_LN6XeulTZQCKdQYbeJm0hxG5Wq8mncDzJpKqpIDCnvuIrnSkPUhWppuldDYYf2Ytr6nKplV-iKh5Txt3__c0R6f8M59GsgZ1j0oLihHRrNknrWN2gaS_YsLf1yHNWLotqAtUDHQGsaH256oKWBfWv6CtUG4o8lvIR9XqZrpPIO4_mBX4dFp_1sFLRJ-_jx7fhU1DLKQRLhOkqsCwWUs0jCUxqHSue5kwbYUJlo9ByBlygc2xkpIMzLSwCmcFwzXnCdQqcn5JOURZwRmg6T0KbJ7lBt8YgncIVsnQrrbYCTfKc9Nx6zVa7GzNm9VJd_G2-JIfOG7sm2CvSqdZbuCYH-qtabtY33s3finSkaA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEG4QTfSECsa3PXh0ZbfdR-sVJBiREEXDjSztlHBwIbD4-52WZY2JF2-b2aTZdHbyfZ1HP0JutYySicKfVzKtvVDEGmPOt2ceYEz5IgyZdmITSb8vRiM5qJC7chYGAFzzGdzbR1fL13O1tqmypkA6gwx9h-xGuEawmdYqMyoczzKxLGsIzOqvuFpnzL1YBrJse5fN1sfg1bZ12eTKL1kVhyqd2v--55A0fsbz6KAEniNSgeyY1Ao-SYtoXaFpK9mwtdVJbzCfZXkf8gfaBljQ4nrVKZ1n1L2ib5CvKDJZytvUKWbaXiLnPppmuDpMP4txpaxB3juPw1bXKwQVvBkCde4ZFiZCTgIBTCgVSh6nTOlE-9IEvuEMeILuMYEWFtBUYhDKNAZsyiOuYuD8hFSzeQanhMaTyDdplGp0bAjCalwhTzfCKJOgSZyRut2v8WJzZ8a42Krzv803ZL87fOmNe0_95wtyYD2zaYm9JNV8uYYrsqe-8tlqee1c_g1J6Kev |
| 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%3Ajournal&rft.genre=proceeding&rft.title=2017+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29&rft.atitle=PointNet%3A+Deep+Learning+on+Point+Sets+for+3D+Classification+and+Segmentation&rft.au=Charles%2C+R.+Qi&rft.au=Hao+Su&rft.au=Mo+Kaichun&rft.au=Guibas%2C+Leonidas+J.&rft.date=2017-07-01&rft.pub=IEEE&rft.issn=1063-6919&rft.eissn=1063-6919&rft.spage=77&rft.epage=85&rft_id=info:doi/10.1109%2FCVPR.2017.16&rft.externalDocID=8099499 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1063-6919&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1063-6919&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1063-6919&client=summon |