R3D：Residual Reliability Routing for Training-Free Image-to-3D Acceleration

Recent diffusion- and flow-based image-to-3D generators synthesize high-quality 3D assets, but remain computationally expensive due to repeated Transformer computation throughout sampling. Existing training-free acceleration methods reduce this cost by exploiting temporal, stage-wise, or token-wise redundancy, yet they do not characterize whether the residual updates from Transformer blocks can be reliably predicted for faster inference. In this paper, we first characterize the reliability of predicting these residual updates within the texture stage. We find that prediction reliability varies substantially across sampling time and network depth, while the relative ordering of reliable and unreliable locations remains consistent across inputs. Moreover, fine-grained residual units within a Transformer block can remain predictable even when whole-block residual prediction is unreliable. Motivated by these findings, we propose Residual Reliability Routing (R3D), a training-free framework that uses the reliability of residual prediction to construct a reusable routing policy. R3D first constructs reusable reliability profiles from a small calibration set through Residual Reliability Profiling (RRP). Then, we propose Granularity-Aware Routing (GAR), which selects the residual unit within a Transformer block that reduces the most computation. Finally, Exact-Anchor Refresh (EAR) recomputes selected residual units in the Transformer block to refresh prediction histories. The resulting routing policy is fixed before inference and generalized to unseen inputs without modifying the pretrained generator. Experimental results show that R3D consistently reduces texture stage inference cost while largely preserving 3D generation quality.