RayDer is a unified transformer backbone for self-supervised static-scene novel view synthesis that absorbs dynamic content as a nuisance factor and shows power-law scaling with data and compute while matching supervised methods in zero-shot settings.
No pose at all: Self-supervised pose-free 3D Gaussian splatting from sparse views.arXiv preprint arXiv:2508.01171,
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CanonicalGS aggregates view-centric evidence into a canonical latent world with uncertainty-aware fusion to improve novel view synthesis and downstream perception tasks.
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RayDer: Scalable Self-Supervised Novel View Synthesis from Real-World Video
RayDer is a unified transformer backbone for self-supervised static-scene novel view synthesis that absorbs dynamic content as a nuisance factor and shows power-law scaling with data and compute while matching supervised methods in zero-shot settings.
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Learning Stable Canonical Worlds for Novel View Synthesis and Beyond
CanonicalGS aggregates view-centric evidence into a canonical latent world with uncertainty-aware fusion to improve novel view synthesis and downstream perception tasks.