Arbi3R: Arbitrary-Scale 3D Super-Resolution in Feed-Forward Novel View Synthesis

Feed-forward novel view synthesis (NVS) is fast and generalizable, but renders at a fixed, low resolution. Recent feed-forward 3D super-resolution (SR) lifts this limit, yet predicts a fixed set of explicit 3D Gaussians, and trains a separate model for each integer scale. Arbitrary-scale SR otherwise exists only in 2D image space or in per-scene 3D NVS. None of the existing methods are at once generalizable and arbitrary-scale. We present Arbi3R, the first feed-forward (FF) framework to treat arbitrary-scale 3D-SR in FF NVS. For this, we replace the explicit Gaussians with an Implicit Function Decoder (IFD) that renders the novel views as a continuous function of pixel coordinates and scales. So, one trained model produces an output at any resolution from sparse, LR input views, including scales unseen at training. A Multi-View Geometry Encoder (MVGE) in our Arbi3R encodes the inputs into scene tokens, and the IFD decodes the target views through its scale-aware Coarse View Renderer (CVR) that produces an LR coarse view, an Epipolar Dual-Stream Detail Aggregator (EDDA) that recovers viewconsistent high-frequency details from the source views along epipolar lines, and a Coordinate-Modulated Neural Operator (CMNO) for continuous upsampling. On RealEstate10K, ACID and DL3DV, our Arbi3R outperforms the latest FF 3DSR specialists (SRSplat, SR3R) by 0.8 to 2.2 dB in PSNR, at their supported fixed scales, while uniquely spanning continuous and unseen resolutions. Arbi3R code will be released upon acceptance.