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MVSplat360: benchmarking 360 generalizable 3D novel view synthesis from sparse views

Abstract:
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. Readers are highly recommended to view the video results at donydchen.github.io/mvsplat360.
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author
ORCID:
0000-0003-1374-2858


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Funder identifier:
https://ror.org/0439y7842
Grant:
EP/Z001811/1
Programme:
SYN3D


Publisher:
Curran Associates
Host title:
Advances in Neural Information Processing Systems 37 (NeurIPS 2024)
Volume:
2
Pages:
107064-107086
Publication date:
2025-02-01
Acceptance date:
2024-09-25
Event title:
38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
Event location:
Vancouver, BC, Canada
Event website:
https://neurips.cc/Conferences/2024
Event start date:
2024-12-10
Event end date:
2024-12-15
ISSN:
1049-5258
ISBN:
9798331314385


Language:
English
Pubs id:
2081409
Local pid:
pubs:2081409
Deposit date:
2025-01-29

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