TripoSR: Fast 3D Object Reconstruction from a Single Image
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866907819795546112 |
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| author | Tochilkin, Dmitry Pankratz, David Liu, Zexiang Huang, Zixuan Letts, Adam Li, Yangguang Liang, Ding Laforte, Christian Jampani, Varun Cao, Yan-Pei |
| author_facet | Tochilkin, Dmitry Pankratz, David Liu, Zexiang Huang, Zixuan Letts, Adam Li, Yangguang Liang, Ding Laforte, Christian Jampani, Varun Cao, Yan-Pei |
| contents | This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in under 0.5 seconds. Building upon the LRM network architecture, TripoSR integrates substantial improvements in data processing, model design, and training techniques. Evaluations on public datasets show that TripoSR exhibits superior performance, both quantitatively and qualitatively, compared to other open-source alternatives. Released under the MIT license, TripoSR is intended to empower researchers, developers, and creatives with the latest advancements in 3D generative AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TripoSR: Fast 3D Object Reconstruction from a Single Image Tochilkin, Dmitry Pankratz, David Liu, Zexiang Huang, Zixuan Letts, Adam Li, Yangguang Liang, Ding Laforte, Christian Jampani, Varun Cao, Yan-Pei Computer Vision and Pattern Recognition This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in under 0.5 seconds. Building upon the LRM network architecture, TripoSR integrates substantial improvements in data processing, model design, and training techniques. Evaluations on public datasets show that TripoSR exhibits superior performance, both quantitatively and qualitatively, compared to other open-source alternatives. Released under the MIT license, TripoSR is intended to empower researchers, developers, and creatives with the latest advancements in 3D generative AI. |
| title | TripoSR: Fast 3D Object Reconstruction from a Single Image |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.02151 |