Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
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.
67d2c2a8876050b1ae724978
Jiale Wang
admin_assigned
2025-03-14T11:22:43.181Z
64b739796ab5d14ca7f0b90d
/avatars/04595b44855ecd9b23bb06366fffd5f4.svg
Jiale Wang
user
jialewang
67d2c2a8876050b1ae724979
Chen Zhao
admin_assigned
2025-03-14T11:23:07.065Z
67249efa005972700d9f6575
/avatars/ade096f83eeeeb94120e867ea413650e.svg
Chen Zhao
user
Sailorzzcc
67d2c2a8876050b1ae72497a
Wei Ke
67d2c2a8876050b1ae72497b
Tong Zhang
Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
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.
A Monte Carlo diffusion mechanism enhances the robustness and generalization of learning-based RANSAC by simulating noisy data distributions.
diffusion-based paradigm Monte Carlo sampling generalization ability learning-based RANSAC ScanNet MegaDepth