RayMap3R: Inference-Time RayMap for Dynamic 3D Reconstruction

Streaming feed-forward 3D reconstruction enables real-timejoint estimation of scene geometry and camera poses from RGB images.However, without explicit dynamic reasoning, streaming models can beaffected by moving objects, causing artifacts and drift. In this work, wepropose RayMap3R, a training-free streaming framework for dynamicscene reconstruction. We observe that RayMap-based predictions exhibita static-scene bias, providing an internal cue for dynamic identification.Based on this observation, we construct a dual-branch inference schemethat identifies dynamic regions by contrasting RayMap and image pre-dictions, suppressing their interference during memory updates. We fur-ther introduce reset metric alignment and state-aware smoothing to pre-serve metric consistency and stabilize predicted trajectories. Our methodachieves state-of-the-art performance among streaming approaches ondynamic scene reconstruction across multiple benchmarks. The projectpage and code are available at https://raymap3r.github.io/.