arXiv:2605.31535v1 Announce Type: cross Abstract: Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of training on realistic videos and the hard-to-predict scaling behavior of multi-network system designs. We introduce RayDer, a unified, feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering into a single backbone, turning self-supervised NVS into a well-posed single-model scaling problem. A minimal dynamic state, treated as a nuisance factor, absorbs time-varyi
The rapid advancements in transformer architectures and self-supervised learning are enabling more unified and scalable approaches to complex AI tasks like novel view synthesis.
This development represents a significant step towards more robust and scalable 3D scene understanding from raw video, crucial for embodied AI and digital twins.
Traditional multi-network systems for novel view synthesis are being replaced by unified transformer models, simplifying the scaling problem and improving consistency.
- · 3D content creators
- · Robotics companies
- · Metaverse platforms
- · AI hardware manufacturers
- · Companies relying on brittle multi-network NVS systems
- · Traditional 3D modeling pipelines
RayDer improves the efficiency and scalability of generating 3D environments from video data.
Enhanced 3D scene understanding accelerates the development and deployment of more capable embodied AI agents and realistic simulations.
The widespread availability of high-fidelity volumetric video and digital twins could transform industries from e-commerce to urban planning.
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Read at arXiv cs.LG