LaRI3R: Bidirectional Ray Intersections for Occluded 3D Scene Reconstruction from Two Views

Recovering complete 3D scene geometry from limited observations remains challenging due to severe occlusions and restricted viewpoints. Although observing a scene from many viewpoints can progressively improve reconstruction completeness, dense multi-view reconstruction often requires costly exploration and computation. Recent single-view feedforward methods address the trade-off between reconstruction quality and efficiency through layered representations, but they still suffer from strong ambiguity in heavily occluded regions. Extending layered reconstruction to two views is also non-trivial because occlusion ordering becomes view-dependent, and visually corresponding evidence can be weak or absent across views. We present LaRI3R, a new feedforward two-view framework for complete 3D scene reconstruction under severe occlusions. Our approach is built on two core designs: 1) We introduce a bidirectional layered representation that predicts geometry in both forward and backward ray orders, which reduces geometric inconsistencies in heavily occluded regions. 2) Instead of relying purely on appearance-based matching, we perform geometry-aware feature aggregation using explicit 3D positional cues. This design enables geometrically related but visually dissimilar regions to communicate across views. Experiments on both object- and scene-level benchmarks show that LaRI3R outperforms representative generative, iterative, and feedforward methods while preserving efficient inference. We further show that the recovered occluded geometry serves as a strong structural cue for downstream 3D vision tasks such as camera pose estimation with notable improvements over existing large geometric models.