TTT3R: 3D Reconstruction as Test-Time Training
Authors: name: Xingyu Chen, name: Yue Chen, name: Yuliang Xiu, name: Andreas Geiger, name: Anpei Chen

Modern Recurrent Neural Networks have become a competitive architecture for
3D reconstruction due to their linear-time complexity. However, their
performance degrades significantly when applied beyond the training context
length, revealing limited length generalization. In this work, we revisit the
3D reconstruction foundation models from a Test-Time Training perspective,
framing their designs as an online learning problem. Building on this
perspective, we leverage the alignment confidence between the memory state and
incoming observations to derive a closed-form learning rate for memory updates,
to balance between retaining historical information and adapting to new
observations. This training-free intervention, termed TTT3R, substantially
improves length generalization, achieving a 2times improvement in global
pose estimation over baselines, while operating at 20 FPS with just 6 GB of GPU
memory to process thousands of images. Code available in
https://rover-xingyu.github.io/TTT3R