TriRender: Scaling Neural Rendering via Reweighted Hierarchical Attention

Transformer-based neural renderers can synthesize globally illuminated images of unseen scenes, but dense attention limits their scalability. Moreover, tessellation increases the number of triangle tokens, whose features and attention logits may remain similar to those of the original triangles. Nonuniform tessellation can therefore shift softmax attention toward subdivided regions and alter predictions even when the surface geometry is unchanged. This token-count dependence also affects hierarchical attention, where coarse tokens replace groups of triangles to reduce computational cost. We propose \papername, a two-stage neural rendering pipeline that addresses these coupled challenges through reweighted hierarchical attention. Inspired by continuous attention, we use represented surface area as a quadrature weight, giving triangles and clusters a common measure across discretizations and hierarchy levels. This reduces the dependence of attention on token count when coarse and fine representations are combined. We couple this reweighting with a spatially cohesive 3D hierarchy, where grouping nearby triangles helps limit information loss when they are represented by a single coarse token. Regenerating coarse tokens from their constituent triangle features at each layer keeps them tied to local surface regions without maintaining independent cluster states. Using this hierarchy, the view-independent encoder combines attention to all coarse tokens with sliding-window triangle attention to encode global light transport, while the view-dependent decoder retains coarse global context and retrieves fine triangle features relevant to each image patch. Experiments demonstrate improved generalization to more complex scenes and substantially reduced inference time while maintaining rendering quality.