RenderFormer++: Scalable and Physically Grounded Feed-Forward Neural Rendering
This work addresses the scalability and physical consistency bottlenecks in Transformer-based neural rendering for global illumination, enabling practical feed-forward rendering of complex scenes.
RenderFormer++ introduces Physics-Informed Transport Guidance and Hierarchical Object-Centric Tokenization to achieve scalable, physically consistent feed-forward neural rendering for global illumination, outperforming prior methods in accuracy and efficiency across complex large-scale scenes.
We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural rendering methods such as RenderFormer achieve promising cross-scene generalization, but suffer from limited physical consistency and poor scalability due to the quadratic attention complexity of triangle-level tokenization. To address these issues, we introduce Physics-Informed Transport Guidance (PITG), which embeds rendering-equation inductive biases into the attention mechanism and enforces transport consistency loss, enabling physically consistent light transport modeling. We further propose Hierarchical Object-Centric Tokenization (HOCT), which aggregates triangle-level features into compact object-level tokens via cross-attention with learnable queries, substantially reducing computational and memory costs while preserving geometric and radiometric information. Extensive experiments demonstrate that RenderFormer++ achieves scalable, stable, and generalizable feed-forward global illumination rendering across complex large-scale scenes with improved physical accuracy and efficiency over prior neural rendering methods.