Bake It Till You Make It: Ultrafast Spatial Texture-Atlas Splatting

Gaussian Splatting (GS) with neural radiance representations achieves high-fidelity color detail, but the per-frame network evaluations impose a heavy rendering cost. This is an obstacle for real-time and resource-constrained applications such as VR, gaming, and mobile rendering, where sustained high frame rates are essential. We present a learned sparse scene representation built on 2D primitives together with a novel baking method that adaptively converts the neural component into a single %global, hardware-accelerated texture atlas, eliminating network inference at runtime. Our 2D surfels carry low-frequency geometry and view-dependent appearance, while view-independent, per-primitive high-frequency texture is encoded with a spatial hash grid and distilled into the texture atlas. A sparsity objective that penalizes per-primitive falloffs yields a substantially more compact representation than prior methods. Our approach renders roughly $7$ to $9\times$ faster than 3DGS on common benchmarks, and over an order of magnitude faster on individual scenes, while surpassing the reconstruction quality and speed of the fastest sparsification methods (FastGS and Speedy-Splat). A quality-optimized variant matches the perceptual quality of the strongest baseline while still rendering several times faster. Replacing software with hardware rasterization pushes frame rates beyond $3000$ FPS with no loss in quality, making high-quality neural scene rendering practical on commodity hardware.