RayDer

RayDer: Scalable Self-Supervised Novel View Synthesis via Unified Feed-Forward Transformer

RayDer is a unified feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering into a single backbone for self-supervised novel view synthesis (NVS). By treating dynamic content as a nuisance factor absorbed by a minimal dynamic state, it enables stable training on unconstrained real-world video without requiring dynamic-scene reconstruction. The model exhibits clean power-law scaling with both data and compute across multiple model sizes, and achieves zero-shot open-set performance competitive with supervised state-of-the-art methods on multiple benchmarks.