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Sim(3)-TurboReg: Robust and Efficient Scale-Aware Registration via Tri-Scale Consistency

/ Paper arXiv 2611.14135

Key numbers

90%On controlled unknown-scale registration at 90% outliers
92.5%ours matches RANSAC accuracy with 92.5% lower median latency
22.2%ours improves mean ATE RMSE by 22.2% and reduces mean end-to-end time by 16.6%

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8 / 100#66 of 1200 papers that day, from GitHub, Hugging Face, Hacker News and Semantic Scholar, looked up the morning it was ranked.

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Abstract

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Feed-forward 3D foundation models recover geometry at high fidelity, yet inference memory constraints keep them from processing long sequences natively. Current streaming systems work around this limit with sliding-window chunking, which introduces severe monocular scale ambiguity. Resolving this ambiguity requires Sim(3) registration that stays accurate and efficient under outlier contamination. We introduce Sim(3)-TurboReg, which reformulates the compatibility graph construction of TurboReg by replacing absolute distance constraints with a strict Tri-Scale Consistency (TSC) criterion. Our analysis shows that TSC suppresses both mismatching and coordinate-distortion outliers. We integrate the resulting solver into existing streaming systems and obtain high-fidelity, kilometer-scale reconstruction for feed-forward models. On controlled unknown-scale registration at 90% outliers, ours matches RANSAC accuracy with 92.5% lower median latency. In DA3-Streaming on KITTI, ours improves mean ATE RMSE by 22.2% and reduces mean end-to-end time by 16.6%. Code: https://github.com/ivan-zzj/Sim3-TurboReg

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