MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views

TL;DR

MVSplat360 is a novel feed-forward method for 360-degree scene synthesis from sparse views, combining geometry-aware reconstruction with pre-trained diffusion models to produce photorealistic, 3D-consistent views.

Contribution

It introduces MVSplat360, a new approach that integrates 3D Gaussian Splatting with Stable Video Diffusion for high-quality 360 view synthesis from minimal input views.

Findings

Outperforms state-of-the-art methods on DL3DV-10K benchmark.

Supports rendering arbitrary views with as few as 5 sparse input views.

Achieves superior visual quality in 360-degree novel view synthesis.

Abstract

We introduce MVSplat360, a feed-forward approach for 360{\deg} 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{\deg} 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.