RetroSplat: Retroactive Revision of Streaming Gaussian Maps with Replay Evidence

A streaming Gaussian map begins with partial observations and receives new evidence as the camera moves. RetroSplat uses this evidence to revise Gaussians that have already been published. Each Gaussian belongs to a persistent voxel cell that retains per-frame feature statistics and accumulates evidence for revision. Later depths vote for surface support or free space, and renders into seen cameras yield opacity replay gradients. After every six-frame packet, a decoder trained across scenes reads this evidence and revises committed Gaussians without a per-scene optimizer; a causal encoder publishes new cells between revisions. On four RealEstate10K and ACID protocols, the final maps beat the append-only ReCoSplat by 1.0–2.0 dB PSNR. Training without replay costs 1.6–2.8 dB, most on the earliest-observed RealEstate10K content. Doubling the RealEstate10K views from 64 to 128 over the same span changes final-map PSNR by +0.01 dB, versus -0.91 dB for ReCoSplat. An optional post-stream refit adds 1.85–2.17 dB.