MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929582231257088 |
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| author | Chen, Yuedong Zheng, Chuanxia Xu, Haofei Zhuang, Bohan Vedaldi, Andrea Cham, Tat-Jen Cai, Jianfei |
| author_facet | Chen, Yuedong Zheng, Chuanxia Xu, Haofei Zhuang, Bohan Vedaldi, Andrea Cham, Tat-Jen Cai, Jianfei |
| contents | We introduce MVSplat360, a feed-forward approach for 360° novel view synthesis (NVS) of diverse real-world scenes, using only sparse observations. This setting is inherently ill-posed due to minimal overlap among input views and insufficient visual information provided, making it challenging for conventional methods to achieve high-quality results. Our MVSplat360 addresses this by effectively combining geometry-aware 3D reconstruction with temporally consistent video generation. Specifically, it refactors a feed-forward 3D Gaussian Splatting (3DGS) model to render features directly into the latent space of a pre-trained Stable Video Diffusion (SVD) model, where these features then act as pose and visual cues to guide the denoising process and produce photorealistic 3D-consistent views. Our model is end-to-end trainable and supports rendering arbitrary views with as few as 5 sparse input views. To evaluate MVSplat360's performance, we introduce a new benchmark using the challenging DL3DV-10K dataset, where MVSplat360 achieves superior visual quality compared to state-of-the-art methods on wide-sweeping or even 360° NVS tasks. Experiments on the existing benchmark RealEstate10K also confirm the effectiveness of our model. The video results are available on our project page: https://donydchen.github.io/mvsplat360. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04924 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views Chen, Yuedong Zheng, Chuanxia Xu, Haofei Zhuang, Bohan Vedaldi, Andrea Cham, Tat-Jen Cai, Jianfei Computer Vision and Pattern Recognition We introduce MVSplat360, a feed-forward approach for 360° novel view synthesis (NVS) of diverse real-world scenes, using only sparse observations. This setting is inherently ill-posed due to minimal overlap among input views and insufficient visual information provided, making it challenging for conventional methods to achieve high-quality results. Our MVSplat360 addresses this by effectively combining geometry-aware 3D reconstruction with temporally consistent video generation. Specifically, it refactors a feed-forward 3D Gaussian Splatting (3DGS) model to render features directly into the latent space of a pre-trained Stable Video Diffusion (SVD) model, where these features then act as pose and visual cues to guide the denoising process and produce photorealistic 3D-consistent views. Our model is end-to-end trainable and supports rendering arbitrary views with as few as 5 sparse input views. To evaluate MVSplat360's performance, we introduce a new benchmark using the challenging DL3DV-10K dataset, where MVSplat360 achieves superior visual quality compared to state-of-the-art methods on wide-sweeping or even 360° NVS tasks. Experiments on the existing benchmark RealEstate10K also confirm the effectiveness of our model. The video results are available on our project page: https://donydchen.github.io/mvsplat360. |
| title | MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.04924 |