Multi-camera odometryIntermediate
StreamRig: Exploiting Intra-Rig Geometry for Streaming Multi-Camera Odometry
This preprint presents StreamRig, a freeze-and-stream system that adapts a frozen multi-view 3D foundation model into causal streaming odometry for calibrated multi-camera rigs. Across NCLT, TartanGround, KITTI-360, and a self-collected humanoid-robot dataset, it achieves the lowest translation and rotation drift among non-oracle methods, and on an RTX 4090 it processes a five-camera rig in 26.2 ms with 2.6 GiB, faster and lighter than monocular CUT3R. The result matters because it shows how lightweight trainable modules can exploit known rig geometry and temporal state without retraining the foundation front-end, including sim-to-real transfer.