Validation over Contexts: Robust Long-Context Inference for 3D Reconstruction Models

Feed-forward 3D reconstruction recovers cameras and scene geometry from un-ordered image collections in a single pass but its reliability deteriorates once the input is no longer curated. Real captures, internet albums and retrieval results routinely contain distractor views from other scenes, near-duplicates and visually similar but geometrically incompatible images, which silently corrupt cross-view geometry reasoning. Cleaning such inputs is non-trivial at scale. Jointly examining all images exceeds practical memory budgets while judging each image from a single bounded window is brittle. A valid view can appear unsupported simply because its overlapping neighbors are not in that window. An effective input-side filter must therefore be memory-bounded, robust to distractors and careful to retain the clean views that downstream reconstruction depends on. We resolve this trade-off by repeatedly validating each image against a small set of scene-consistent anchors within bounded, memory-feasible subsets of the collection. We retain an image if any anchor in any subset provides convincing support. We realize this as Multi-Anchor Context Validation (MACV), a training-free front-end that requires no retraining of the reconstruction backbone. Across multiple datasets and backbones, MACV consistently improves filtering quality and downstream camera pose, depth and point-cloud reconstruction, and remains feasible on long noisy collections where prior single-context robust filtering is not. Codes will be released upon acceptance.