arXiv Computer Vision
Sep 15

Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping

Anchor3R is a streaming 3D reconstruction framework that predicts window-relative poses and local geometry in the current‑frame coordinate system, forming a dense relative‑pose graph for online pose updates and loop‑aware motion averaging. It improves long‑horizon pose accuracy and dense reconstruction quality on indoor, outdoor, driving, and RGB‑D benchmarks, and generalizes from 48‑frame training sequences to streams exceeding 10,000 frames while keeping GPU memory bounded. The method addresses issues of train‑test mismatch, early‑anchor bias, and accumulated drift found in previous streaming models.

By Peilin Tao, Chong Cheng, Yuansen Du, Caiwei Song, Zhengqing Chen, Xiaoyang Guo, Wei Yin, Weiqiang Ren, Qian Zhang, Hainan Cui, Shuhan Shen
arXiv AI
Sep 10

FILT3R: Latent State Adaptive Kalman Filter for Streaming 3D Reconstruction

FILT3R is a training‑free latent filtering layer for streaming 3D reconstruction that treats recurrent state updates as stochastic state estimation in token space. It maintains per‑token variance and computes a Kalman‑style gain to balance memory retention with new observations, estimating process noise online from temporal drift of candidate tokens. Experiments show that FILT3R generalizes overwrite and gating policies, shrinking gains in stable regimes and increasing them during genuine scene changes, thereby improving long‑horizon stability for depth, pose, and 3D reconstruction.

By Seonghyun Jin, Jong Chul Ye