RADIANT: Object-Centric Feed-Forward Global Illumination

Feed-forward neural renderers predict physically grounded global illumination across unseen 3D scenes in a single forward pass, bypassing expensive Monte Carlo path tracing and per-scene optimization. However, existing methods rely on tokenizing explicit triangle meshes, causing attention complexity to scale quadratically with primitive count. Macroscopic light transport operates predominantly between surface patches and objects rather than diffusing facet by facet across individual triangles; polygon tessellation is an artifact of representation rather than transport physics. We introduce RADIANT (RADiance-based Inter-Actor Neural Transformer), an object-centric architecture that learns mesh-free feed-forward global illumination directly from point-sampled surfaces. By pooling surface points into a small number of patch tokens per object, RADIANT decouples attention complexity from polygon discretization. Light transport is modeled in two stages: an inter-object scene transformer and a view decoder. A factored radiometric attention bias injects directional alignment and material compatibility via rotary phases without explicit pairwise matrices, while an invariant form-factor extension incorporates pairwise geometric exchange invariants directly as $\mathrm{SE}(3)$-invariant inner products. To resolve primary visibility without mesh connectivity, camera rays cross-attend scene tokens guided by depth-tested point-splat hit channels in a shared spherical frame. Across 1.03M scenes under matched capacity, RADIANT achieves 25 dB in 5× fewer steps and surpasses RenderFormer by 3.0 dB (and RenderFormer-V2 by 7.2 dB) at 300k steps. On scenes with hundreds of objects, RADIANT avoids quadratic primitive bottlenecks and maintains interactive rendering rates. We will release our code and data upon publication.