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Pengcheng Laboratory
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Unreliable Gaussians are the root cause of artifacts in sparse-view 3D Gaussian Splatting, and modeling their reliability in both the optimization and observation domains dramatically improves reconstruction quality.
Forget balanced multimodal learning – letting the best modality lead the way actually unlocks better performance.
Reconstructing 3D scenes from images obscured by smoke and extreme darkness is now significantly more achievable, thanks to insights gleaned from the NTIRE 2026 challenge.
Compressing 3D Gaussian Splatting just got a whole lot better: GeoHCC maintains geometric integrity and rendering fidelity by explicitly modeling inter-anchor geometric correlations, outperforming existing anchor-based approaches.
MLLMs are often overconfident, but a new confidence-driven training and test-time scaling approach can boost accuracy by 8.8% across benchmarks.
