When Epipolar Transformers Meets Implicit Neural Super-Resolution in Multi-View Stereo

Learning-based Multi-View Stereo (MVS) methods heavily rely on feature extraction and cost volume construction to bridge 2D semantics and 3D spatial associations. However, many recent studies have increasingly shifted focus away from these two critical steps, often adopting a multi-stage cascaded framework that can propagate errors from earlier stages. To this end, we introduce TTINS-MVSNet, a novel progressive refinement framework that integrates a one-stage MVS module enhanced by two types of Epipolar Transformer (ET) and subsequent depth optimization modules. Specifically, we incorporate an intra-view ET into context-aware feature extraction and an inter-view ET into visibility-aware cost aggregation. A significant innovation in our approach is the introduction of implicit neural super-resolution module, designed to recover finer details. Experimental results on benchmark datasets demonstrate that our method outperforms all previous approaches with similar structures, highlighting its effectiveness and generalization capability. The code will be available after publication.