Robust Multi-Model Fitting through Learning Neighbor Regions

The paper introduces Learning Neighbor Regions (LNR), a coarse‑to‑fine framework for robust multi‑model fitting in noisy environments. LNR’s coarse module uses a neural network to analyze geometric features of minimum sets, selecting a small number of promising hypotheses to improve efficiency. The fine module encodes neighbor region features for each hypothesis, allowing simultaneous refinement and scoring while avoiding differentiation of the sampling process and solvers. Experiments on four classic tasks show state‑of‑the‑art performance and suggest easy adaptation to other robust fitting problems.

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• computer-vision
• benchmarks