title: Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
summary: 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.
paper_authors._id: 67d2c2a8876050b1ae724978
paper_authors.name: Jiale Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:22:43.181Z
paper_authors._id: 64b739796ab5d14ca7f0b90d
paper_authors.avatarUrl: /avatars/04595b44855ecd9b23bb06366fffd5f4.svg
paper_authors.fullname: Jiale Wang
paper_authors.type: user
paper_authors.user: jialewang
paper_authors._id: 67d2c2a8876050b1ae724979
paper_authors.name: Chen Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:23:07.065Z
paper_authors._id: 67249efa005972700d9f6575
paper_authors.avatarUrl: /avatars/ade096f83eeeeb94120e867ea413650e.svg
paper_authors.fullname: Chen Zhao
paper_authors.type: user
paper_authors.user: Sailorzzcc
paper_authors._id: 67d2c2a8876050b1ae72497a
paper_authors.name: Wei Ke
paper_authors._id: 67d2c2a8876050b1ae72497b
paper_authors.name: Tong Zhang
paper_title: Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
paper_summary: 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.
paper_ai_keywords: diffusion-based paradigm Monte Carlo sampling