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keywords: gaussian splatting | ||
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# SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting | ||
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We find that using naive depth priors is not sufficient and integrate depth priors with generative and explicit constraints to reduce background collapse, remove floaters, and enhance consistency from unseen viewpoints | ||
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direct floater pruning(?) | ||
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diffusion models to provide supervision in regions with little coverage from training views | ||
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apply depth constraints similar to prior work | ||
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## Related Work | ||
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1. constraints on the variantion between views | ||
2. depth priors to novel views | ||
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## Methods | ||
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- alpha-blended depth vs. mode-selected depth. The former is a weighted sum of Gaussians' depth, while the latter is most opaque Gaussian's depth | ||
- **depth loss** monocular estimation model predicts relative depth. Instead of estimating scale and shift to align, use Pearson correlation | ||
- between monocular depth and alpha-blended depth | ||
- **SDS loss**(?) | ||
- **floater pruning** when alpha-blended depth and mode-selected depth disagree. (pruning is done manually, case by case) |