Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
Authors: name: Jiale Wang, name: Chen Zhao, name: Wei Ke, name: Tong Zhang

Random Sample Consensus (RANSAC) is a fundamental approach for robustly
estimating parametric models from noisy data. Existing learning-based RANSAC
methods utilize deep learning to enhance the robustness of RANSAC against
outliers. However, these approaches are trained and tested on the data
generated by the same algorithms, leading to limited generalization to
out-of-distribution data during inference. Therefore, in this paper, we
introduce a novel diffusion-based paradigm that progressively injects noise
into ground-truth data, simulating the noisy conditions for training
learning-based RANSAC. To enhance data diversity, we incorporate Monte Carlo
sampling into the diffusion paradigm, approximating diverse data distributions
by introducing different types of randomness at multiple stages. We evaluate
our approach in the context of feature matching through comprehensive
experiments on the ScanNet and MegaDepth datasets. The experimental results
demonstrate that our Monte Carlo diffusion mechanism significantly improves the
generalization ability of learning-based RANSAC. We also develop extensive
ablation studies that highlight the effectiveness of key components in our
framework.