ArtiFixer: Using Auto-Regressive Diffusion Models to Improve 3D Reconstruction Quality
Summary
ArtiFixer introduces a method for enhancing and extending 3D reconstructions using auto-regressive diffusion models. The approach addresses limitations of per-scene optimization methods like 3D Gaussian Splatting, which produce high-quality novel view synthesis but struggle with under-observed areas. By leveraging generative priors, ArtiFixer corrects artifacts in poorly observed regions, improving the quality and completeness of 3D reconstructions.
Source
Twitter / XArtiFixer: Using Auto-Regressive Diffusion Models to Improve 3D Reconstruction Qualitynvda.wsKey quotes
· 2 pulledPer-scene optimization methods such as 3D Gaussian Splatting provide state-of-the-art novel view synthesis quality but extrapolate poorly to under-observed areas.
Methods that leverage generative priors to correct artifacts in these areas hold promise
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