iSplat: Iterative Learning for Fine-Grained Gaussian Splatting

Abstract

Recent advances in feed-forward 3D Gaussian splatting have demonstrated remarkable efficiency by reconstructing scenes in a single pass. However, the reconstruction fidelity of these methods lags behind that of traditional optimization-based approaches, which gradually correct reconstruction flaws through a lengthy iterative process. In this paper, we leverage the strengths of both paradigms and introduce iSplat, a novel framework that reformulates reconstruction as an iterative feed-forward process involving multiple (typically three) passes. Central to iSplat is a recurrent GRU-based optimizer that refines both geometry and appearance in a synergistic loop. To address geometric inaccuracies, we propose an uncertainty-driven depth refinement strategy that progressively narrows the search space for each Gaussian based on its estimated uncertainty from the previous step. To further improve appearance details, we design a region-aware enhancement mechanism that applies targeted multi-view and monocular feature aggregation to resolve ambiguities in both overlapping and non-overlapping areas. We validate iSplat's robustness and generalization on in-domain (RealEstate10K, ACID) and cross-dataset (DTU, ACID) benchmarks. With only 42.6M parameters, iSplat surpasses DepthSplat (354M) on RealEstate10K (PSNR: 27.67 vs. 27.47 dB). Crucially, on the cross-dataset DTU benchmark, it further boosts the PSNR by 2.88 dB (18.26 vs. 15.38 dB), showcasing exceptional generalization. These results highlight the significant potential of iterative refinement to overcome the inherent limitations of one-shot approaches. Our code is available at https://github.com/haifengwu205/iSplat.