FILT3R: Latent State Adaptive Kalman Filter for Streaming 3D Reconstruction
Streaming 3D reconstruction maintains a persistent latent state that is updated online from incoming frames, enabling constant-memory inference. A key failure mode is the state update rule: aggressive overwrites forget useful history, while conservative updates fail to track new evidence, and both behaviors become unstable beyond the training horizon. To address this challenge, we propose FILT3R, a training-free latent filtering layer that casts recurrent state updates as stochastic state estimation in token space. FILT3R maintains a per-token variance and computes a Kalman-style gain that adaptively balances memory retention against new observations. Process noise -- governing how much t...
| Authors | Seonghyun Jin, Jong Chul Ye |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 5 September 2026 |
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