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MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
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abstract
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we build a cost volume representation via plane sweeping, where the cross-view feature similarities stored in the cost volume can provide valuable geometry cues to the estimation of depth. We also learn other Gaussian primitives' parameters jointly with the Gaussian centers while only relying on photometric supervision. We demonstrate the importance of the cost volume representation in learning feed-forward Gaussians via extensive experimental evaluations. On the large-scale RealEstate10K and ACID benchmarks, MVSplat achieves state-of-the-art performance with the fastest feed-forward inference speed (22~fps). More impressively, compared to the latest state-of-the-art method pixelSplat, MVSplat uses $10\times$ fewer parameters and infers more than $2\times$ faster while providing higher appearance and geometry quality as well as better cross-dataset generalization.
Forward citations
Cited by 12 Pith papers
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A decoder-only Transformer with causal attention and cached past-frame features performs incremental 3D reconstruction from streaming images, beating the RNN-based CUT3R on several benchmark metrics.
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UniForward: Unified 3D Scene and Semantic Field Reconstruction via Feed-Forward Gaussian Splatting from Only Sparse-View Images
UniForward is a feed-forward model that reconstructs 3D Gaussian scenes with embedded semantic features from just two unposed images, enabling real-time novel-view rendering and open-vocabulary segmentation.
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MonoSplat: Generalizable 3D Gaussian Splatting from Monocular Depth Foundation Models
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FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation
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