PointInfinity: Resolution-Invariant Point Diffusion Models
We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution poin...
Uložené v:
| Vydané v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 10050 - 10060 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
16.06.2024
|
| Predmet: | |
| ISSN: | 1063-6919 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly, we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models, demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points, 31× more than Point-E) with state-of-the-art quality. |
|---|---|
| AbstractList | We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly, we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models, demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points, 31× more than Point-E) with state-of-the-art quality. |
| Author | Huang, Zixuan Rehg, James M. Wu, Chao-Yuan Debnath, Shoubhik Johnson, Justin |
| Author_xml | – sequence: 1 givenname: Zixuan surname: Huang fullname: Huang, Zixuan organization: FAIR at Meta – sequence: 2 givenname: Justin surname: Johnson fullname: Johnson, Justin organization: FAIR at Meta – sequence: 3 givenname: Shoubhik surname: Debnath fullname: Debnath, Shoubhik organization: FAIR at Meta – sequence: 4 givenname: James M. surname: Rehg fullname: Rehg, James M. organization: University of Illinois at Urbana-Champaign – sequence: 5 givenname: Chao-Yuan surname: Wu fullname: Wu, Chao-Yuan organization: FAIR at Meta |
| BookMark | eNotj99KwzAcRqMoOGffYBd9gdZf_ie7k-q0MHEM9XYkTQKRmkrTCXt7i3r1XZyPA-caXaQheYRWGGqMQd8277s9J5LSmgBhNYDm6gwVWmpFOVBOAcQ5WmAQtBIa6ytU5PwBAJRgLLRaoPVuiGlqU4gpTqd1ufd56I9THFLVpm8zRpOm8vdT3scQjnkm5fPgfJ9v0GUwffbF_y7R2-bhtXmqti-PbXO3rSKWYqocNyC9lIQxzJ1yrAvgLbWGc-GYMNJaoqwKncDKMM47o1gnLPfBkblB0iVa_Xmj9_7wNcZPM54Oc9Ms4Jj-AK7rSzQ |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/CVPR52733.2024.00958 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| EISBN | 9798350353006 |
| EISSN | 1063-6919 |
| EndPage | 10060 |
| ExternalDocumentID | 10655651 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IH 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP OCL RIE RIL RIO |
| ID | FETCH-LOGICAL-i176t-d5a07e7724415d8d4cf0eb3ba556d46a7bb28b8fc618a455ca84c6b5efd250373 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001342442401037&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:00:59 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i176t-d5a07e7724415d8d4cf0eb3ba556d46a7bb28b8fc618a455ca84c6b5efd250373 |
| PageCount | 11 |
| ParticipantIDs | ieee_primary_10655651 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-June-16 |
| PublicationDateYYYYMMDD | 2024-06-16 |
| PublicationDate_xml | – month: 06 year: 2024 text: 2024-June-16 day: 16 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) |
| PublicationTitleAbbrev | CVPR |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003211698 |
| Score | 2.2798243 |
| Snippet | We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 10050 |
| SubjectTerms | 3D Diffusion Model 3D Generation 3D reconstruction 3D Vision Computer vision Deep Learning Noise reduction Point cloud compression Shape Three-dimensional displays Training Transformer cores |
| Title | PointInfinity: Resolution-Invariant Point Diffusion Models |
| URI | https://ieeexplore.ieee.org/document/10655651 |
| WOSCitedRecordID | wos001342442401037&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/eLvHCXMwlV1LSwMxEA5aPHiqj4pvcvC6urt591otFqQsotJbyWMCC7KV7rbgvzfZrtWLB29LWEiYZJLvm-SbQejGK5r7sO0nDKhPKFM0UdRFIKfAZ-CA2jaJ65OYTuVspopOrN5qYQCgfXwGt_Gzvct3C7uKobLg4ZwFABLIzq4QYiPW2gZUSKAyXMlOHpel6m70VjzH_GIk0MA8JslWsbD7ryIq7Rky7v-z9wM0-FHj4WJ7zhyiHaiOUL-Dj7hzzvoYDYtFWTWTypfBTT-HOEbmN-sqmVTrwImDEXH7D74vvV_FOBmOtdDe6wF6HT-8jB6TrjRCUmaCN4ljOhUQkHGkQ046an0aaLHRYYSOci2MyaWR3vJMasqY1ZJabhh4FzAPEeQE9apFBacIZ9KnhOSapwqoMkYBc4Ry6cHozDByhgbRFvOPTfaL-bcZzv9ov0D70dzxOVXGL1GvWa7gCu3ZdVPWy-t2zr4Apv6YBg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA6igp7qo-LbPXhd3Wwem_RaLS3WskiV3kqymcCCbKXdFvz3JulavXjwtoSFhEkm-b5JvhmEbq2kqXXbfsyA2pgySWNJjQdyEiwGA7QISVyH2WgkJhOZN2L1oIUBgPD4DO78Z7jLN7Ni6UNlzsM5cwDEkZ0dRmmK13KtTUiFODLDpWgEcjiR9923_MVnGCOOCKY-Tbb0pd1_lVEJp0iv9c_-D1D7R48X5ZuT5hBtQXWEWg2AjBr3XByjTj4rq3pQ2dI56mcn8rH59cqKB9XKsWJnxij8Ez2U1i59pCzy1dDeF2302nscd_txUxwhLnHG69gwlWTgsLEnREYYWtjEEWOt3AgN5SrTOhVa2IJjoShjhRK04JqBNQ71kIycoO1qVsEpirCwCSGp4okEKrWWwAyhXFjQCmtGzlDb22L6sc5_Mf02w_kf7Tdorz9-Hk6Hg9HTBdr3pvePqzC_RNv1fAlXaLdY1eVifh3m7wvANptN |
| 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=Proceedings+%28IEEE+Computer+Society+Conference+on+Computer+Vision+and+Pattern+Recognition.+Online%29&rft.atitle=PointInfinity%3A+Resolution-Invariant+Point+Diffusion+Models&rft.au=Huang%2C+Zixuan&rft.au=Johnson%2C+Justin&rft.au=Debnath%2C+Shoubhik&rft.au=Rehg%2C+James+M.&rft.date=2024-06-16&rft.pub=IEEE&rft.eissn=1063-6919&rft.spage=10050&rft.epage=10060&rft_id=info:doi/10.1109%2FCVPR52733.2024.00958&rft.externalDocID=10655651 |