Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
This addresses the problem of robust parametric model estimation in computer vision, particularly for feature matching, but is incremental as it builds on existing learning-based RANSAC approaches.
The paper tackles the limited generalization of learning-based RANSAC methods to out-of-distribution data by introducing a Monte Carlo diffusion paradigm that injects noise into ground-truth data for training, resulting in significantly improved generalization ability as demonstrated on ScanNet and MegaDepth datasets.
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.